Patent application title: Methods For Molecular Toxicology Modeling
Inventors:
James C. Diggans (Gaithersburg, MD, US)
Michael Elashoff (Gaithersburg, MD, US)
IPC8 Class: AG06F1900FI
USPC Class:
702 19
Class name: Data processing: measuring, calibrating, or testing measurement system in a specific environment biological or biochemical
Publication date: 2008-11-13
Patent application number: 20080281526
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Patent application title: Methods For Molecular Toxicology Modeling
Inventors:
James C. Diggans
Michael Elashoff
Agents:
COOLEY GODWARD KRONISH LLP;ATTN: Patent Group
Assignees:
Origin: WASHINGTON, DC US
IPC8 Class: AG06F1900FI
USPC Class:
702 19
Abstract:
The present invention is based on methods of predicting toxicity of test
agents and methods of generating toxicity prediction models using
algorithms for analyzing quantitative gene expression information. The
invention also includes computer systems comprising the toxicity
prediction models, as well as methods of using the computer systems by
remote users for determining the toxicity of test agents.Claims:
1. A method of predicting at least one toxic effect of a test agent
comprising:(a) providing nucleic acid hybridization data for a plurality
of genes from at least one cell or tissue sample exposed to the test
agent;(b) converting the hybridization data from at least one gene to a
gene expression measure;(c) generating a gene regulation score from the
gene expression measure for said at least one gene;(d) generating a
sample prediction score for the agent; and(e) comparing the sample
prediction score to a toxicity reference prediction score, thereby
predicting at least one toxic effect of the test agent.
2. A method of claim 1, wherein at least one cell or tissue sample is exposed to a test agent vehicle.
3. A method of claim 2, wherein the converting of step (b) comprises normalizing the hybridization data for background hybridization and for test agent vehicle induced expression.
4. A method of claim 2, wherein the gene expression measure is a gene fold-change value.
5. A method of claim 4, wherein the fold-change value is calculated by a log scale linear additive model.
6. A method of claim 5, wherein the log scale linear additive model is a robust multi-array average (RMA).
7. A method of claim 1, wherein the nucleic acid hybridization data has been screened by a quality control process that measures outlier data.
8. A method of claim 1, wherein step (c) comprises dimensional reduction using Partial Least Squares (PLS).
9. A method of claim 1, wherein the sample prediction score is generated with a weighted index score for each gene.
10. A method of 1, wherein the sample prediction score for the agent is generated from the gene regulation score for said at least one gene.
11. A method of claim 10, wherein the sample prediction score for the agent is generated from the gene regulation score for at least about 10 genes.
12. A method of claim 10, wherein the sample prediction score for the agent is generated from the gene regulation score for at least about 50 genes.
13. A method of claim 10, wherein the sample prediction score for the agent is generated from the gene regulation score for at least about 100 genes.
14. A method of claim 1, wherein the toxicity reference prediction score is generated by a method comprising:(a) providing nucleic acid hybridization data for a plurality of genes from at least one cell or tissue sample exposed to a toxin and at least one cell or tissue sample exposed to the toxin vehicle;(b) converting the hybridization data from at least one gene to fold-change values;(c) generating a gene regulation score from the fold-change value for said at least one gene; and(d) generating a toxicity reference prediction score for the toxin.
15. A method of claim 1, wherein step (a) comprises loading nucleic acid hybridization data to a server via a remote connection.
16. A method of claim 15, wherein the remote connection is over the Internet.
17. A method of claim 1, wherein the toxicity reference prediction score is provided in a database.
18. A method of claim 17, wherein the toxicity reference prediction score is derived from a toxicology model
19. A method of claim 18, wherein the toxicology model is selected from the group consisting of an individual toxin model, a toxin class model, a general toxicology model and a tissue pathology model.
20. A method of claim 1, further comprising:(f) generating a report comprising information related to the toxic effect.
21. A method of claim 20, wherein the report comprises information related to the mechanism of the toxic effect.
22. A method of claim 20, wherein the report comprises information related to the toxins used to prepare the toxicity reference prediction score.
23. A method of 20, wherein the report comprises information related to at least one similarity between the test agent and a toxin.
24. A method of claim 16, wherein the hybridization data is contained in a plain text file.
25. A method of claim 16, wherein the hybridization data is contained in a CEL file.
26. A method of claim 1, wherein the nucleic acid hybridization data is annotated with information selected from the group consisting of customer data, cell or tissue sample data, hybridization technology data and test agent data.
27. A method of claim 15, wherein step (a) further comprises selecting at least one toxicity model to predict said at least one toxic effect.
28. A method of providing a report comprising a prediction of at least one toxic effect of a test agent comprising:(a) receiving nucleic acid hybridization data for a plurality of genes from at least one cell or tissue sample exposed to the test agent and at least one cell or tissue sample exposed to the test agent vehicle to a server via a remote link;(b) converting the hybridization data from at least one gene to robust multi-array average (RMA) fold-change values;(c) generating a gene regulation score from the RMA fold-change value for said at least one gene;(d) generating a sample prediction score for the agent;(e) comparing the sample prediction score to a toxicity reference prediction score; and(f) providing a report comprising information related to said at least one toxic effect.
29. A method of creating a toxicology model comprising:(a) providing nucleic acid hybridization data for a plurality of genes from at least one cell or tissue sample exposed to a toxin;(b) converting the hybridization data from at least one gene to a gene expression measure;(c) generating a gene regulation score from gene expression measure for said at least one gene;(d) generating a toxicity reference prediction score for the toxin, thereby creating a toxicology model.
30. A method of claim 29, wherein at least one cell or tissue sample is exposed to a test agent vehicle.
31. A method of claim 29, wherein the converting of step (b) comprises normalizing the hybridization data for background hybridization and for test agent vehicle induced expression.
32. A method of claim 29, wherein the gene expression measure is a gene fold-change value.
33. A method of claim 32, wherein the fold-change value is calculated by a log scale linear additive model.
34. A method of claim 33, wherein the log scale linear additive model is a robust multi-array average (RMA).
35. A method of claim 29, wherein the generating of step (c) comprises dimensional reduction using Partial Least Squares (PLS).
36. A method of claim 29, wherein step (d) comprises the generation of a weighted index score for each gene.
37. A method of claim 29, wherein the toxicity reference prediction score for the toxin is generated from the gene regulation score for said at least one gene.
38. A method of claim 37, wherein the toxicity reference prediction score for the agent is generated from the gene regulation score for at least about 10 genes.
39. A method of claim 37, wherein the toxicity reference prediction score for the agent is generated from the gene regulation score for at least about 50 genes.
40. A method of claim 37, wherein the toxicity reference prediction score for the agent is generated from the gene regulation score for at least about 100 genes.
41. A method of claim 29, wherein the toxicology model is selected from the group consisting of an individual toxin model, a toxin class model, a general toxicology model and a tissue pathology model.
42. A method of claim 29, further comprising validating the model.
43. A method of claim 42, wherein the validation comprises using a cross-validation procedure.
44. A method of claim 43, wherein the cross-validation procedure is a 2/3/1/3 validation procedure.
45. A computer system comprising:(a) a computer readable medium comprising a toxicity model for predicting toxicity of a test agent, wherein the toxicity model is generated by a method of claim 29; and(b) software that allows a user to predict at least one toxic effect of a test agent by comparing a sample prediction score to a toxicity reference prediction score in the toxicity model.
46. A computer system of claim 45, wherein the software enables a user to compare quantitative gene expression information obtained from a cell or tissue sample exposed to a test agent to the quantitative gene expression information in the toxicity model to predict whether the test agent is a toxin.
47. A computer system of claim 45, further comprising software that allows a user to transmit from a remote location nucleic acid hybridization data from a cell or tissue sample exposed to a test agent to predict whether the test agent is a toxin.
48. A computer system of claim 45, wherein the nucleic acid hybridization data from the sample may be transmitted via the Internet.
49. A computer system of claim 45, wherein the nucleic acid hybridization data is microarray hybridization data.
50. A computer system of claim 45, wherein the nucleic acid hybridization data is PCR data.
51. A computer system of claim 45, further comprising a data structure comprising at least one toxicity reference prediction score.
52. A computer system of claim 45, wherein the data structure further comprises at least one gene PLS score.
53. A computer system of claim 45, wherein the data structure further comprises at least one gene regulation score.
54. A computer system of claim 45, wherein the data structure further comprises at least one sample prediction score.
55. A computer readable medium comprising a data structure comprising at lest one toxicity reference prediction score and software for accessing said data structure.
Description:
RELATED APPLICATIONS
[0001]This application claims the benefit of U.S. Provisional Application Ser. No. 60/554,981, filed Mar. 22, 2004 and U.S. Provisional Application Ser. No. 60/613,831, filed Sep. 29, 2004, both of which are herein incorporated by reference in their entirety for all purposes. This application also claims priority to PCT Application No. PCT/US03/37556, filed Nov. 24, 2003, which is herein incorporated by reference in its entirety for all purposes.
SEQUENCE LISTING SUBMISSION ON COMPACT DISC
[0002]The Sequence Listing submitted concurrently herewith on compact disc under 37 C.F.R. §§1.821(c) and 1.821(e) is herein incorporated by reference in its entirety. Four copies of the Sequence Listing, one on each of four compact discs are provided. Copy 1, Copy 2 and Copy 3 are identical. Copies 1, 2 and 3 are also identical to the CRF. Each electronic copy of the Sequence Listing was created on Nov. 22, 2004 with a file size of 2398 KB. The file names are as follows: Copy 1--gene logic 5133-wo.txt; Copy 2--gene logic 5133-wo.txt; Copy 3--gene logic 5133-wo.txt; CRF--gene logic 5133-wo.txt.
BACKGROUND OF THE INVENTION
[0003]The need for methods of assessing the toxic impact of a compound, pharmaceutical agent or environmental pollutant on a cell or living organism has led to the development of procedures which utilize living organisms as biological monitors. The simplest and most convenient of these systems utilize unicellular microorganisms such as yeast and bacteria, since they are the most easily maintained and manipulated. In addition, unicellular screening systems often use easily detectable changes in phenotype to monitor the effect of test compounds on the cell. Unicellular organisms, however, are inadequate models for estimating the potential effects of many compounds on complex multicellular animals, as they do not have the ability to carry out biotransformations.
[0004]The biotransformation of chemical compounds by multicellular organisms is a significant factor in determining the overall toxicity of agents to which they are exposed. Accordingly, multicellular screening systems may be preferred or required to detect the toxic effects of compounds. The use of multicellular organisms as toxicology screening tools has been significantly hampered, however, by the lack of convenient screening mechanisms or endpoints, such as those available in yeast or bacterial systems. Additionally, certain previous attempts to produce toxicology prediction systems have failed to provide the necessary modeling data and statistical information to accurately predict toxic responses (e.g., WO 00/12760, WO 00/47761, WO 00/63435, WO 01/32928, and WO 01/38579).
[0005]The pharmaceutical industry spends significant resources to ensure that therapeutic compounds of interest are not toxic to human beings. This process is lengthy as well as expensive and involves testing in a series of organisms starting with rats and progressing to dogs or non-human primates. Moreover, modeling methods for designing candidate pharmaceuticals and their synthesis in nucleic acid, peptide or organic compound libraries has increased the need for inexpensive, fast and accurate methods to predict toxic responses. Toxicity modeling methods based on nucleic acid hybridization platforms would allow the use biological samples from compound-exposed animal or cell culture samples, such as rats or rat hepatocyte cell cultures, to detect human organ toxicity much earlier than has been possible to date.
SUMMARY OF THE INVENTION
[0006]The present invention is based, in part, on the elucidation of the global changes in gene expression in animal tissues or cells, such as liver or kidney tissue or cells, exposed to known toxins, in particular hepatotoxins or renal toxins, as compared to unexposed tissues or cells, as well as the identification of individual genes that are differentially expressed upon toxin exposure.
[0007]In various aspects, the invention includes methods of predicting at least one toxic effect of a test agent by comparing gene expression information from agent-exposed samples to a database of gene expression information from toxin-exposed and control samples (vehicle-exposed samples or samples exposed to a non-toxic compound or low levels of a toxic compound). These methods comprise providing or generating quantitative gene expression information from the samples, converting the gene expression information to matrices of fold-change values by a robust multi-array average (RMA) algorithm, generating a gene regulation score for each gene that is differentially expressed upon exposure to the test agent by a partial least squares (PLS) algorithm, and calculating a sample prediction score for the test agent. This sample prediction score is then compared to a reference prediction score for one or more toxicity models. If the sample prediction score is equal to or greater than the reference prediction score, the test agent can be predicted to have at least one toxic effect or to produce at least one pathology corresponding to the toxicity model to which the test agent's prediction score is compared.
[0008]In various aspects, the invention includes methods of creating a toxicology model. These methods comprise providing or generating quantitative nucleic acid hybridization data for a plurality of genes from at least one cell or tissue sample exposed to a toxin and at least one cell or tissue sample exposed to the toxin vehicle, converting the hybridization data from at least one gene to a gene expression measure, such as fold-change value, by a robust multi-array average (RMA) algorithm, generating a gene regulation score from a gene expression measure for at least one gene by a partial least squares (PLS) algorithm, and generating a toxicity reference prediction score for the toxin, thereby creating a toxicology model.
[0009]In other aspects, the invention includes a computer system comprising a computer readable medium containing a toxicity model for predicting the toxicity of a test agent and software that allows a user to predict at least one toxic effect of a test agent by comparing a sample prediction score for the test agent to a toxicity reference prediction score for the toxicity model.
[0010]In further aspects of the invention, the gene expression information from test agent-exposed tissues or cells may be prepared as text or binary files, such as CEL files, and transmitted via the Internet for analysis and comparisons to the toxicity models stored on a remote, central server. After processing, the user that sent the text files receives a report indicating the toxicity or non-toxicity of the test agent.
[0011]In other aspects of the invention, the user may download one or more toxicity models from the remote, central server, as well as software for manipulating the user's data and the toxicity models, to a local server. Gene expression information from test agent-exposed tissues or cells may then be prepared as text files, such as CEL files, and analyzed and compared at the user's site to the toxicity models stored on the local server. After processing, the software generates a report indicating the toxicity or non-toxicity of the test agent.
TABLES
[0012]Table 1: Table 1 provides the GLGC identifier (fragment names from Table 2) in relation to the SEQ ID NO. and GenBank Accession number for each of the gene fragments listed in Table 2 (all of which are herein incorporated by reference and replication in the attached sequence listing). The gene names and Unigene cluster titles are also included.
[0013]Table 2: Table 2 presents the PLS scores (weighted gene index scores) from an exemplary kidney general toxicity model.
DETAILED DESCRIPTION
Definitions
[0014]As used herein, "nucleic acid hybridization data" refers to any data derived from the hybridization of a sample of nucleic acids to a one or more of a series of reference nucleic acids. Such reference nucleic acids may be in the form of probes on a microarray or set of beads or may be in the form of primers that are used in polymerization reactions, such as PCR amplification, to detect hybridization of the primers to the sample nucleic acids. Nucleic hybridization data may be in the form of numerical representations of the hybridization and may be derived from quantitative, semi-quantitative or non-quantitative analysis techniques or technology platforms. Nucleic acid hybridization data includes, but is not limited to gene expression data. The data may be in any form, including florescence data or measurements of florescence probe intensities from a microarray or other hybridization technology platform. The nucleic acid hybridization data may be raw data or may be normalized to correct for, or take into account, background or raw noise values, including background generated by microarray high/low intensity spots, scratches, high regional or overall background and raw noise generated by scanner electrical noise and sample quality fluctuation.
[0015]As used herein, "cell or tissue samples" refers to one or more samples comprising cell or tissue from an animal or other organism, including laboratory animals such as rats or mice. The cell or tissue sample may comprise a mixed population of cells or tissues or may be substantially a single cell or tissue type, such as hepatocytes or liver tissue. Cell or tissue samples as used herein may also be in vitro grown cells or tissue, such as primary cell cultures, immortalized cell cultures, cultured hepatocytes, cultured liver tissue, etc. Cells or tissue may be derived from any organ, including but not limited to, liver, kidney, cardiac, muscle (skeletal or cardiac) or brain.
[0016]As used herein, "test agent" refers to an agent, compound or composition that is being tested or analyzed in a method of the invention. For instance, a test agent may be a pharmaceutical candidate for which toxicology data is desired.
[0017]As used herein, "test agent vehicle" refers to the diluent or carrier in which the test agent is dissolved, suspended in or administered in, to an animal, organism or cells.
[0018]As used herein, "toxin vehicle" refers to the diluent or carrier in which a toxin is dissolved, suspended in or administered in, to an animal, organism or cells.
[0019]As used herein, a "gene expression measure" refers to any numerical representation of the expression level of a gene or gene fragment in a cell or tissue sample. A "gene expression measure" includes, but is not limited to, a fold-change value.
[0020]As used herein, "at least one gene" refers to a nucleic acid molecule detected by the methods of the invention in a sample. The term "gene" as used herein, includes fully characterized open reading frames and the encoded mRNA as well as fragments of expressed RNA that are detectable by any hybridization method in the cell or tissue samples assayed as described herein. For instance, a "gene" includes any species of nucleic acid that is detectable by hybridization to a probe in a microarray, such as the "genes" of Table 1. As used herein, at least one gene includes a "plurality of genes."
[0021]As used herein, "fold-change value" refers to a numerical representation of the expression level of a gene, genes or gene fragments between experimental paradigms, such as a test or treated cell or tissue sample, compared to any standard or control. For instance, a fold-change value may be presented as microarray-derived florescence or probe intensities for a gene or genes from a test cell or tissue sample compared to a control, such as an unexposed cell or tissue sample or a vehicle-exposed cell or tissue sample. An RMA fold-change value as described herein is a non-limiting example of a fold-change value calculated by methods of the invention.
[0022]As used herein, "gene regulation score" refers to a quantitative measure of gene expression for a gene or gene fragment as derived from a weighted index score or PLS score for each gene and the fold-change value from treated vs. control samples.
[0023]As used herein, "sample prediction score" refers to a numerical score produced via methods of the invention as herein described. For instance, a "sample prediction score" may be calculated using the PLS weight or PLS score for at least one gene in a gene expression profile generated from the sample and the RMA fold-change value for that same gene. A "sample prediction score" is derived from summing the individual gene regulation scores calculated for a given sample.
[0024]As used herein, "toxicity reference prediction score" refers to a numerical score generated from a toxicity model that can be used as a cut-off score to predict at least one toxic effect of a test agent. For instance, a sample prediction score can be compared to a toxicity reference prediction score to determine if the sample score is above or below the toxicity reference prediction score. Sample prediction scores falling below the value of a toxicity reference prediction score are scored as not exhibiting at least one toxic effect and sample prediction scores above the value if a toxicity reference prediction score are scored as exhibiting at least one toxic effect.
[0025]As used herein, a log scale linear additive model includes any log-liner model such as log scale robust multi-array average or RMA (Irizarry et al., Nucleic Acids Research 31(4) e15 (2003).
[0026]As used herein, "remote connection" refers to a connection to a server by a means other than a direct hard-wired connection. This term includes, but is not limited to, connection to a server through a dial-up line, broadband connection, Wi-Fi connection, or through the Internet.
[0027]As used herein, a "CEL file" refers to a file that contains the average probe intensities associated with a coordinate position, cell or feature on a microarray (such information provided by the CDF or ILQ file). See Affymetrix GeneChip® Expression Analysis Technical Manual, which is herein
[0028]As used herein, a "gene expression profile" comprises any quantitative representation of the expression of at least one mRNA species in a cell sample or population and includes profiles made by various methods such as differential display, PCR, microarray and other hybridization analysis, etc.
[0029]Methods of Generating Toxicity Models
[0030]To evaluate and identify gene expression changes that are predictive of toxicity, studies using selected compounds with well characterized toxicity may be used to build a model or database of the present invention. Methods of the present invention include an RMA/PLS method (analysis of raw gene expression data by the robust multi-array average algorithm, with evaluation of predictive ability by the partial least squares algorithm) to create models and databases for predicting toxicity.
[0031]In general, cell and tissue samples are analyzed after exposure to compounds known to exhibit at least one toxic effect. Low doses of these compounds, or the vehicles in which they were prepared, are used as negative controls. Compounds that are known not to exhibit at least one toxic effect may also be used as negative controls.
[0032]In the present invention, a toxicity study or "tox study" comprises a set of cell or tissue samples that have been exposed to one or more toxins and may include matched samples exposed to the toxin vehicle or a low, non-toxic, dose of the toxin. As described below, the cell or tissue samples may be exposed to the toxin and control treatments in vivo or in vitro. In some studies, toxin and control exposure to the cell or tissue samples may take place by administering an appropriate dose to an animal model, such as a laboratory rat. In some studies, toxin and control exposure to the cell or tissue samples may take place by administering an appropriate dose to a sample of in vitro grown cells or tissue, such as primary rat or human hepatocytes. These samples are typically organized into cohorts by test compound, time (for instance, time from initial test compound dosage to time at which rats are sacrificed), and dose (amount of test compound administered). All cohorts in a tox study typically share the same vehicle control. For example, a cohort may be a set of samples from rats that were treated with acyclovir for 6 hours at a high dosage (100 mg/kg). A time-matched vehicle cohort is a set of samples that serve as controls for treated animals within a tox study, e.g., for 6-hour acyclovir-treated high dose samples the time-matched vehicle cohort would be the 6-hour vehicle-treated samples with that study.
[0033]A toxicity database or "tox database" is a set of tox studies that alone or in combination comprise a reference database. For instance, a reference database may include data from rat tissue and cell samples from rats that were treated with different test compounds at different dosages and exposed to the test compounds for varying lengths of time.
[0034]RMA, or robust multi-array average, is an algorithm that converts raw fluorescence intensities, such as those derived from hybridization of sample nucleic acids to an Affymetrix GeneChip® microarray, into expression values, one value for each gene fragment on a chip (Irizarry et al. (2003), Nucleic Acids Res. 31(4):e15, 8 pp.; and Irizarry et al. (2003) "Exploration, normalization, and summaries of high density oligonucleotide array probe level data," Biostatistics 4(2): 249-264). RMA produces values on a log 2 scale, typically between 4 and 12, for genes that are expressed significantly above or below control levels. These RMA values can be positive or negative and are centered around zero for a fold-change of about 1. A matrix of gene expression values generated by RMA can be subjected to PLS to produce a model for prediction of toxic responses, e.g., a model for predicting liver or kidney toxicity. In a preferred embodiment, the model is validated by techniques known to those skilled in the art. Preferably, a cross-validation technique is used. In such a technique, the data is randomly broken into training and test sets several times until model success rate is determined. Most preferably, such technique uses 2/3/1/3 cross-validation, where 1/3 of the data is dropped and the other 2/3 is used to rebuild the model.
[0035]PLS, or Partial Least Squares, is a modeling algorithm that takes as inputs a matrix of predictors and a vector of supervised scores to generate a set of prediction weights for each of the input predictors (Nguyen et al. (2002), Bioinformatics 18:39-50). These prediction weights are then used to calculate a gene regulation score to indicate the ability of each analyzed gene to predict a toxic response. As described in the examples, the gene regulation scores may then be used to calculate a toxicity reference prediction score.
[0036]From the nucleic acid hybridization data, a gene expression measure is calculated for one or more genes whose level of expression is detected in the nucleic acid hybridization value. As described above, the gene expression measure may comprise an RMA fold-change value. The toxicity reference score=ΣwiRFCi. "i" is the index number for each gene in a gene expression profile to be evaluated. "wi" is the PLS weight (or PLS score, see Table 2) for each gene. "RFCi" is the RMA fold-change value for the ith gene, as determined from a normalized RMA matrix of gene expression data from the sample (described above). The PLS weight multiplied by the RMA fold-change value gives a gene regulation score for each gene, and the regulation scores for all the individual genes are added to give a toxicity reference prediction score for a sample or cohort of sample. A toxicity reference prediction score can be calculated from at least one gene regulation score, or at least about 5, 10, 25, 50, 100, 500 or about 1,000 or more gene regulation scores.
[0037]In one embodiment of the invention, a toxicology or toxicity model of the invention is prepared or created by the steps of (a) providing nucleic acid hybridization data for a plurality of genes from at least one cell or tissue sample exposed to a toxin and at least one cell or tissue sample exposed to the toxin vehicle; (b) converting the hybridization data from at least one gene to a gene expression measure; (c) generating a gene regulation score from gene expression measure for said at least one gene; and (d) generating a toxicity reference prediction score for the toxin, thereby creating a toxicology model. The gene expression measure may be a gene fold-change value calculated by a log scale linear additive model such as RMA and the toxicity reference prediction score may be generated with PLS. The toxicity reference prediction score may then be added to a toxicity model or database and be used to predict at least one toxic effect of an unknown test agent or compound.
[0038]In another preferred embodiment, the model is validated by techniques known to those skilled in the art. Preferably, a cross-validation technique is used. In such a technique, the data is randomly broken into training and test sets several times until an acceptable model success rate is determined. Most preferably, such technique uses 2/3/1/3 cross-validation, where 1/3 of the data is dropped and the other 2/3 is used to rebuild the model.
Methods of Predicting Toxic Effects
[0039]The gene regulation scores and toxicity prediction scores derived from cell or tissue samples exposed to toxins may be used to predict at least one toxic effect, including the hepatotoxicity, renal toxicity or other tissue toxicity of a test or unknown agent or compound. The gene regulation scores and toxicity prediction scores from cell or tissue samples exposed to toxins may also be used to predict the ability of a test agent or compound to induce a tissue pathology, such as liver necrosis, in a sample. The toxicology prediction methods of the invention are limited only by the availability of the appropriate toxicology model and toxicology prediction scores. For instance, the prediction methods of a given system, such as a computer system or database of the invention, can be expanded simply by running new toxicology studies and models of the invention using additional toxins or specific tissue pathology inducing agents and the appropriate cell or tissue samples.
[0040]As used, herein, at least one toxic effect includes, but is not limited to, a detrimental change in the physiological status of a cell or organism. The response may be, but is not required to be, associated with a particular pathology, such as tissue necrosis. Accordingly, the toxic effect includes effects at the molecular and cellular level. Hepatotoxicity, for instance, is an effect as used herein and includes but is not limited to the pathologies of: cholestasis, genotoxicity/carcinogenesis, hepatitis, human-specific toxicity, induction of liver enlargement, steatosis, macrovesicular steatosis, microvesicular steatosis, necrosis, non-1-genotoxic/non-carcinogenic toxicity, peroxisome proliferation, rat non-genotoxic toxicity, and general hepatotoxicity.
[0041]In general, assays to predict the toxicity of a test agent (or compound or multi-component composition) comprise the steps of exposing a cell or tissue sample or population of cell or tissue samples to the test agent or compound, providing nucleic acid hybridization data for at least one gene from the test agent exposed cell or tissue sample(s), by, for instance, assaying or measuring the level of relative or absolute gene expression of one or more of the genes, such as one or more of the genes in Table 2, calculating a sample prediction score and comparing the sample prediction score to one or more toxicology reference scores (see Example 1).
[0042]Sample prediction scores may be calculated as follows: sample prediction score=1 wiRFCi. "i" is the index number for each gene in a gene expression profile to be evaluated. "wi" is the PLS weight (or PLS score) for each gene derived from a toxicity model. "RFCi" is the RMA fold-change value for the ith gene, as determined from a normalized RMA matrix of gene expression data from the sample (described above). The PLS weight from a given model multiplied by the RMA fold-change value gives a gene regulation score for each gene, and the regulation scores for all the individual genes are added to give a prediction score for the sample.
[0043]Nucleic acid hybridization data may include any measurement of the hybridization, including gene expression levels, of sample nucleic acids to probes corresponding to about (or at least) 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 50, 75, 100, 200, 500, 1000 or more genes, or ranges of these numbers, such as about 2-10, about 10-20, about 20-50, about 50-100, about 100-200, about 200-500 or about 500-1000 genes. Nucleic acid hybridization data for toxicity prediction may also include the measurement of nearly all the genes in a toxicity model. "Nearly all" the genes may be considered to mean at least 80% of the genes in any one toxicity model.
[0044]The methods of the invention to predict at least one toxic effect of a test agent or compound may be practiced by one individual or at one location, or may be practiced by more than one individual or at more than one location. For instance, methods of the invention include steps wherein the exposure of a test agent or compound to a cell or tissue sample(s) is accomplished in one location, nucleic acid processing and the generation of nucleic acid hybridization data takes place at another location and gene regulation and sample prediction scores calculated or generated at another location.
[0045]In another embodiment of the invention, cell or tissue samples are exposed to a test agent or compound by administering the agent to laboratory rats and nucleic acids are processed from selected tissues and hybridized to a microarray to produce nucleic acid hybridization data. The nucleic acid hybridization data is then sent to a remote server comprising a toxicology reference database and software that enables generation of individual gene regulation scores and one or more sample prediction scores from the nucleic acid hybridization data. The software may also enable a user to pre-select specific toxicology models and to compare the generated sample prediction scores to one or more toxicology reference scores contained within a database of such scores. The user may then generate or order an appropriate output product(s) that presents or represents the results of the data analysis, generation of gene regulation scores, sample prediction scores and/or comparisons to one or more toxicology reference scores.
[0046]Data, including nucleic acid hybridization data, may be transmitted to a server via any means available, including a secure direct dial-up or a secure or unsecured Internet connection. Toxicology prediction reports or any result of the methods herein may also be transmitted via these same mechanisms. For instance, a first user may transmit nucleic acid hybridization data to a remote server via a secure password protected Internet link and then request transmission of a toxicology report from the server via that same Internet link.
[0047]Data transmitted by a remote user of a toxicity database or model may be raw, un-normalized data or may be normalized from various background parameters before transmission. For instance, data from a microarray may be normalized for various chip and background parameters such as those described above, before transmission. The data may be in any form, as long as the data can be recognized and properly formatted by available software or the software provided as part of a database or computer system. For instance, microarray data may be provided and transmitted in a .cel file or any other common data files produced from the analysis of microarray based hybridization on commercially available technology platforms (see, for instance, the Affymetrix GeneChip® Expression Analysis Technical Manual available at www.affymetrix.com). Such files may or may not be annotated with various information, for instance, but not limited to, information related to the customer or remote user, cell or tissue sample data or information, hybridization technology or platform on which the data was generated and/or test agent data or information.
[0048]Once data is received, the nucleic acid hybridization data may be screened for database compatibility by any available means. In one embodiment, commonly available data quality control metrics can be applied. For instance, outlier analysis methods or techniques may be utilized to identify samples incompatible with the database, for instance, samples exhibiting erroneous florescence values from control probes which are common between the data and the database or toxicity model. In addition, various data QC metrics can be applied, including one or more disclosed in PCT/US03/24160, filed Aug. 1, 2003, which claims priority to U.S. provisional application 60/399,727.
Cell or Tissue Sample Preparation
[0049]As described above, the cell population that is exposed to the test agent, compound or composition may be exposed in vitro or in vivo. For instance, cultured or freshly isolated liver cells, in particular rat hepatocytes, may be exposed to the agent under standard laboratory and cell culture conditions. In another assay format, in vivo exposure may be accomplished by administration of the agent to a living animal, for instance a laboratory rat.
[0050]Procedures for designing and conducting toxicity tests in in vitro and in vivo systems are well known, and are described in many texts on the subject, such as Loomis et al., Loomis's Essentials of Toxicology, 4th Ed., Academic Press, New York, 1996; Echobichon, The Basics of Toxicity Testing, CRC Press, Boca Raton, 1992; Frazier, editor, In Vitro Toxicity Testing, Marcel Dekker, New York, 1992; and the like.
[0051]In in vitro toxicity testing, two groups of test organisms are usually employed. One group serves as a control, and the other group receives the test compound in a single dose (for acute toxicity tests) or a regimen of doses (for prolonged or chronic toxicity tests). Because, in some cases, the extraction of tissue as called for in the methods of the invention requires sacrificing the test animal, both the control group and the group receiving compound must be large enough to permit removal of animals for sampling tissues, if it is desired to observe the dynamics of gene expression through the duration of an experiment.
[0052]In setting up a toxicity study, extensive guidance is provided in the literature for selecting the appropriate test organism for the compound being tested, route of administration. dose ranges, and the like. Water or physiological saline (0.9% NaCl in water) is the solute of choice for the test compound since these solvents permit administration by a variety of routes. When this is not possible because of solubility limitations, vegetable oils such as corn oil or organic solvents such as propylene glycol may be used.
[0053]Regardless of the route of administration, the volume required to administer a given dose is limited by the size of the animal that is used. It is desirable to keep the volume of each dose uniform within and between groups of animals. When rats or mice are used, the volume administered by the oral route generally should not exceed about 0.005 ml per gram of animal. Even when aqueous or physiological saline solutions are used for parenteral injection the volumes that are tolerated are limited, although such solutions are ordinarily thought of as being innocuous. The intravenous LD50 of distilled water in the mouse is approximately 0.044 ml per gram and that of isotonic saline is 0.068 ml per gram of mouse. In some instances, the route of administration to the test animal should be the same as, or as similar as possible to, the route of administration of the compound to man for therapeutic purposes.
[0054]When a compound is to be administered by inhalation, special techniques for generating test atmospheres are necessary. The methods usually involve aerosolization or nebulization of fluids containing the compound. If the agent to be tested is a fluid that has an appreciable vapor pressure, it may be administered by passing air through the solution under controlled temperature conditions. Under these conditions, dose is estimated from the volume of air inhaled per unit time, the temperature of the solution, and the vapor pressure of the agent involved. Gases are metered from reservoirs. When particles of a solution are to be administered, unless the particle size is less than about 2 μm the particles will not reach the terminal alveolar sacs in the lungs. A variety of apparati and chambers are available to perform studies for detecting effects of irritant or other toxic endpoints when they are administered by inhalation. The preferred method of administering an agent to animals is via the oral route, either by intubation or by incorporating the agent in the feed.
[0055]When the agent is exposed to cells in vitro or in cell culture, the cell population to be exposed to the agent may be divided into two or more subpopulations, for instance, by dividing the population into two or more identical aliquots. In some preferred embodiments of the methods of the invention, the cells to be exposed to the agent are derived from liver tissue. For instance, cultured or freshly isolated rat hepatocytes may be used.
[0056]The methods of the invention may be used generally to predict at least one toxic response, and, as described in the Examples, may be used to predict the likelihood that a compound or test agent will induce various specific pathologies, such as liver cholestasis, genotoxicity/carcinogenesis, hepatitis, human-specific toxicity, induction of liver enlargement, steatosis, macrovesicular steatosis, microvesicular steatosis, necrosis, non-genotoxic/non-carcinogenic toxicity, peroxisome proliferation, rat non-genotoxic toxicity, general hepatotoxicity, or other pathologies associated with at least one known toxin. The methods of the invention may also be used to determine the similarity of a toxic response to one or more individual compounds. In addition, the methods of the invention may be used to predict or elucidate the potential cellular pathways influenced, induced or modulated by the compound or test agent.
Databases and Computer Systems
[0057]Databases and computer systems of the present invention typically comprise one or more data structures comprising toxicity or toxicology models as described herein, including models comprising individual gene or toxicology marker weighted index scores or PLS scores (See Table 2), gene regulation scores, sample prediction scores and/or toxicity reference prediction scores. Such databases and computer systems may also comprise software that allows a user to manipulate the database content or to calculate or generate scores as described herein, including individual gene regulation scores and sample prediction scores from nucleic acid hybridization data. Software may also allow a user to predict, assay for or screen for at least one toxic response, including toxicity, hepatotoxicity, renal toxicity, etc, to include gene or protein pathway information and/or to include information related to the mechanism of toxicity, including possible cellular and molecular mechanisms. As an example, software may include at least one element from the Gene Logic ToxShield® Predictive Modeling System such as software comprising at least one algorithm to convert hybridization data from varying platforms, for instance from one microarray platform to a second microarray platform (see U.S. Provisional Application 60/613,831, filed Sep. 29, 2004, which is herein incorporated by reference in its entirety for all purposes).
[0058]As discussed above, the databases and computer systems of the invention may comprise equipment and software that allow access directly or through a remote link, such as direct dial-up access or access via a password protected Internet link.
[0059]Any available hardware may be used to create computer systems of the invention. Any appropriate computer platform, user interface, etc. may be used to perform the necessary comparisons between sequence information, gene or toxicology marker information and any other information in the database or information provided as an input. For example, a large number of computer workstations are available from a variety of manufacturers. Client/server environments, database servers and networks are also widely available and appropriate platforms for the databases of the invention.
[0060]The databases may be designed to include different parts, for instance a sequence database and a toxicology reference database. Methods for the configuration and construction of such databases and computer-readable media containing such databases are widely available, for instance, see U.S. Publication No. 2003/0171876 (Ser. No. 10/090,144), filed Mar. 5, 2002, PCT Publication No. WO 02/095659, published Nov. 23, 2002, and U.S. Pat. No. 5,953,727, which are herein incorporated by reference in their entirety. In a preferred embodiment, the database is a ToxExpress® or BioExpress® database marketed by Gene Logic Inc., Gaithersburg, Md.
[0061]The databases of the invention may be linked to an outside or external database such as GenBank (www ncbi.nlm.nih.gov/entrez.index.html); KEGG (www.genome.ad.jp/kegg); SPAD (www.grt.kyushu-u.ac.jp/spad/index.html); HUGO (www.gene.ucl.ac.uk/hugo); Swiss-Prot (www.expasy.ch.sprot); Prosite (www.expasy.ch/tools/scnpsit1. html); OMIM (www.ncbi.nlm.nih.gov/omim); and GDB (www.gdb.org). In a preferred embodiment, the external database is GenBank and the associated databases maintained by the National Center for Biotechnology Information (NCBI) (www.ncbi.nlm.nih.gov).
Toxicity or Toxicology Reports
[0062]As descried above, the methods, databases and computer systems of the invention can be used to produce, deliver and/or send a toxicity or toxicology report. As consistent with the use of the terms "toxicity" and "toxicology" as used herein, a "toxicity report" and a "toxicology report" are interchangeable.
[0063]The toxicity report of the invention typically comprises information or data related to the results of the practice of a method of the invention. For instance, the practice of a method of identifying at least one toxic effect of a test agent or compound as herein described may result in the preparation or production of a report describing the results of the method including an indication or prediction of at least one toxic response, such as toxicity, hepatotoxicity, renal toxicity, etc. The report may comprise information related to the toxic effects predicted by the comparison of at least one sample prediction score to at least one toxicity reference prediction score from the database as well as other related information such as a literature review or citation list and/or information regarding potential toxicity mechanism(s) of action, etc. The report may also present information concerning the nucleic acid hybridization data, such as the integrity of the data as well as information input by the user of the database and methods of the invention, such as information used to annotate the nucleic acid hybridization data.
[0064]As an exemplary, non-limiting example, a toxicity report of the invention may be in a form such as the reports disclosed in PCT US02/22701, filed Jul. 18, 2002, and U.S. Provisional Application 60/613,831, filed Sep. 29, 2004, both of which are herein incorporated by reference in their entirety for all purposes. As described elsewhere in this specification, the report may be generated by a server or computer system to which is loaded nucleic acid hybridization data by a user. The report related to that nucleic acid data may be generated and delivered to the user via remote means such as a password secured environment available over the Internet or via available computer communication means such as email.
Generating Nucleic Acid Hybridization Data
[0065]Any assay format to detect gene expression may be used to produce nucleic acid hybridization data. For example, traditional Northern blotting, dot or slot blot, nuclease protection, primer directed amplification, RT-PCR, semi- or quantitative PCR, branched-chain DNA and differential display methods may be used for detecting gene expression levels or producing nucleic acid hybridization data. Those methods are useful for some embodiments of the invention. In cases where smaller numbers of genes are detected, amplification based assays may be most efficient. Methods and assays of the invention, however, may be most efficiently designed with high-throughput hybridization-based methods for detecting the expression of a large number of genes.
[0066]To produce nucleic acid hybridization data, any hybridization assay format may be used, including solution-based and solid support-based assay formats. Solid supports containing oligonucleotide probes for differentially expressed genes of the invention can be filters, polyvinyl chloride dishes particles, beads, microparticles or silicon or glass based chips, etc. Such chips, wafers and hybridization methods are widely available, for example, those disclosed by Beattie (WO 95/11755).
[0067]Any solid surface to which oligonucleotides can be bound, either directly or indirectly, either covalently or non-covalently, can be used. A preferred solid support is a high density array or DNA chip. These contain a particular oligonucleotide probe in a predetermined location on the array. Each predetermined location may contain more than one molecule of the probe, but each molecule within the predetermined location has an identical sequence. Such predetermined locations are termed features. There may be, for example, from 2, 10, 100, 1000 to 10,000, 100,000 or 400,000 or more of such features on a single solid support. The solid support, or the area within which the probes are attached may be on the order of about a square centimeter. Probes corresponding to the genes of Tables 1-2 or from the related applications described above may be attached to single or multiple solid support structures, e.g., the probes may be attached to a single chip or to multiple chips to comprise a chip set.
[0068]Oligonucleotide probe arrays, including bead assays or collections of beads, for expression monitoring can be made and used according to any techniques known in the art (see for example, Lockhart et al. (1996), Nat Biotechnol 14:1675-1680; McGall et al. (1996), Proc Nat Acad Sci USA 93: 13555-13460). Such probe arrays may contain at least two or more oligonucleotides that are complementary to or hybridize to two or more of the genes described in Table 2. For instance, such arrays may contain oligonucleotides that are complementary to or hybridize to at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 50, 70, 100, 500 or 1,000 or more of the genes described herein.
[0069]The sequences of the toxicity expression marker genes of Table 2 are in the public databases. Table 1 provides the SEQ ID NO: and GenBank Accession Number (NCBI RefSeq ID) for each of the sequences (see www.ncbi.nlm.nih.gov/), as well as the title for the cluster of which gene is part. The sequences of the genes in GenBank are expressly herein incorporated by reference in their entirety as of the filing date of this application, as are related sequences, for instance, sequences from the same gene of different lengths, variant sequences, polymorphic sequences, genomic sequences of the genes and related sequences from different species, including the human counterparts, where appropriate.
[0070]The terms "background" or "background signal intensity" refer to hybridization signals resulting from non-specific binding, or other interactions, between the labeled target nucleic acids and components of the oligonucleotide array (e.g., the oligonucleotide probes, control probes, the array substrate, etc.). Background signals may also be produced by intrinsic fluorescence of the array components themselves. A single background signal can be calculated for the entire array, or a different background signal may be calculated for each target nucleic acid. In a preferred embodiment, background is calculated as the average hybridization signal intensity for the lowest 5% to 10% of the probes in the array, or, where a different background signal is calculated for each target gene, for the lowest 5% to 10% of the probes for each gene. Of course, one of skill in the art will appreciate that where the probes to a particular gene hybridize well and thus appear to be specifically binding to a target sequence, they should not be used in a background signal calculation. Alternatively, background may be calculated as the average hybridization signal intensity produced by hybridization to probes that are not complementary to any sequence found in the sample (e.g. probes directed to nucleic acids of the opposite sense or to genes not found in the sample such as bacterial genes where the sample is mammalian nucleic acids). Background can also be calculated as the average signal intensity produced by regions of the array that lack any probes at all.
[0071]The phrase "hybridizing specifically to" or "specifically hybridizes" refers to the binding, duplexing, or hybridizing of a molecule substantially to or only to a particular nucleotide sequence or sequences under stringent conditions when that sequence is present in a complex mixture (e.g., total cellular) DNA or RNA.
[0072]As used herein a "probe" is defined as a nucleic acid, capable of binding to a target nucleic acid of complementary sequence through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation. As used herein, a probe may include natural (i.e., A, G, U, C, or T) or modified bases (7-deazaguanosine, inosine, etc.). In addition, the bases in probes may be joined by a linkage other than a phosphodiester bond, so long as it does not interfere with hybridization. Thus, probes may be peptide nucleic acids in which the constituent bases are joined by peptide bonds rather than phosphodiester linkages.
Nucleic Acid Samples
[0073]Cell or tissue samples may be exposed to the test agent in vitro or in vivo. When cultured cells or tissues are used, appropriate mammalian cell extracts, such as liver extracts, may also be added with the test agent to evaluate agents that may require biotransformation to exhibit toxicity. In a preferred format, primary isolates or cultured cell lines of animal or human renal cells may be used.
[0074]The genes which are assayed according to the present invention are typically in the form of mRNA or reverse transcribed mRNA. The genes may or may not be cloned. The genes may or may not be amplified. The cloning and/or amplification do not appear to bias the representation of genes within a population. In some assays, it may be preferable, however, to use polyA+ RNA as a source, as it can be used with fewer processing steps.
[0075]As is apparent to one of ordinary skill in the art, nucleic acid samples used in the methods and assays of the invention may be prepared by any available method or process. Methods of isolating total mRNA are well known to those of skill in the art. For example, methods of isolation and purification of nucleic acids are described in detail in Chapter 3 of Laboratory Techniques in Biochemistry and Molecular Biology, Vol. 24, Hybridization With Nucleic Acid Probes: Theory and Nucleic Acid Probes, P. Tijssen, Ed., Elsevier Press, New York, 1993. Such samples include RNA samples, but also include cDNA synthesized from a mRNA sample isolated from a cell or tissue of interest. Such samples also include DNA amplified from the cDNA, and RNA transcribed from the amplified DNA. One of skill in the art would appreciate that it is desirable to inhibit or destroy RNase present in homogenates before homogenates are used.
[0076]Biological samples may be of any biological tissue or fluid or cells from any organism as well as cells raised in vitro, such as cell lines and tissue culture cells. Frequently the sample will be a tissue or cell sample that has been exposed to a compound, agent, drug, pharmaceutical composition, potential environmental pollutant or other composition. In some formats, the sample will be a "clinical sample" which is a sample derived from a patient. Typical clinical samples include, but are not limited to, sputum, blood, blood-cells (e.g., white cells), tissue or fine needle biopsy samples, urine, peritoneal fluid, and pleural fluid, or cells therefrom. Biological samples may also include sections of tissues, such as frozen sections or formalin fixed sections taken for histological purposes.
Hybridization
[0077]Nucleic acid hybridization simply involves contacting a probe and target nucleic acid under conditions where the probe and its complementary target can form stable hybrid duplexes through complementary base pairing. See WO 99/32660. The nucleic acids that do not form hybrid duplexes are then washed away leaving the hybridized nucleic acids to be detected, typically through detection of an attached detectable label. It is generally recognized that nucleic acids are denatured by increasing the temperature or decreasing the salt concentration of the buffer containing the nucleic acids. Under low stringency conditions (e.g., low temperature and/or high salt) hybrid duplexes (e.g., DNA:DNA, RNA:RNA, or RNA:DNA) will form even where the annealed sequences are not perfectly complementary. Thus, specificity of hybridization is reduced at lower stringency. Conversely, at higher stringency (e.g., higher temperature or lower salt) successful hybridization tolerates fewer mismatches. One of skill in the art will appreciate that hybridization conditions may be selected to provide any degree of stringency.
[0078]In a preferred embodiment, hybridization is performed at low stringency, in this case in 6×SSPET at 37° C. (0.005% Triton X-100), to ensure hybridization and then subsequent washes are performed at higher stringency (e.g., 1×SSPET at 37° C.) to eliminate mismatched hybrid duplexes. Successive washes may be performed at increasingly higher stringency (e.g., down to as low as 0.25×SSPET at 37° C. to 50° C.) until a desired level of hybridization specificity is obtained. Stringency can also be increased by addition of agents such as formamide. Hybridization specificity may be evaluated by comparison of hybridization to the test probes with hybridization to the various controls that can be present (e.g., expression level control, normalization control, mismatch controls, etc.).
[0079]In general, there is a tradeoff between hybridization specificity (stringency) and signal intensity. Thus, in a preferred embodiment, the wash is performed at the highest stringency that produces consistent results and that provides a signal intensity greater than the background intensity. Thus, in a preferred embodiment, the hybridized array may be washed at successively higher stringency solutions and read between each wash. Analysis of the data sets thus produced will reveal a wash stringency above which the hybridization pattern is not appreciably altered and which provides adequate signal for the particular oligonucleotide probes of interest.
Kits
[0080]The invention further includes kits combining, in different combinations, high-density oligonucleotide arrays, reagents for use with the arrays, signal detection and array-processing instruments, toxicology databases and analysis and database management software described above. The kits may be used, for example, to predict or model the toxic response of a test compound.
[0081]The databases that may be packaged with the kits are described above. In particular, the database software and packaged information may contain the databases saved to a computer-readable medium, or transferred to a user's local server. In another format, database and software information may be provided in a remote electronic format, such as a website, the address of which may be packaged in the kit.
[0082]Databases and software designed for use with microarrays are discussed in Balaban et al., U.S. Pat. No. 6,229,911, a computer-implemented method for managing information collected from small or large numbers of microarrays, and U.S. Pat. No. 6,185,561, a computer-based method with data mining capability for collecting gene expression level data, adding additional attributes and reformatting the data to produce answers to various queries. Chee et al., U.S. Pat. No. 5,974,164, disclose a software-based method for identifying mutations in a nucleic acid sequence based on differences in probe fluorescence intensities between wild type and mutant sequences that hybridize to reference sequences.
[0083]Without further description, it is believed that one of ordinary skill in the art can, using the preceding description and the following illustrative examples, make and utilize the compounds of the present invention and practice the claimed methods. The following working examples therefore, specifically point out the preferred embodiments of the present invention, and are not to be construed as limiting in any way the remainder of the disclosure.
EXAMPLES
Example 1
Generation of Toxicity Models Using RMA and PLS
[0084]Various kidney toxins are administered to male Sprague-Dawley rats at various timepoints using administration diluents, protocols and dosing regimes as previously described in the art and previously described in the priority application discussed above.
[0085]As an illustration of the protocols used, the toxins are administered to and animals are sacrificed and kidney samples harvested at the time points indicated below.
Observation of Animals
[0086]1. Clinical cage side observations--twice daily mortality and moribundity check. Skin and fur, eyes and mucous membrane, respiratory system, circulatory system, autonomic and central nervous system, somatomotor pattern, and behavior pattern are checked. Potential signs of toxicity, including tremors, convulsions, salivation, diarrhea, lethargy, coma or other atypical behavior or appearance, are recorded as they occur and include a time of onset, degree, and duration.
[0087]2. Physical Examinations-Prior to randomization, prior to initial treatment, and prior to sacrifice.
[0088]3. Body Weights-Prior to randomization, prior to initial treatment, and prior to sacrifice.
Clinical Pathology
[0089]1. Frequency--Prior to necropsy.
[0090]2. Number of animals--All surviving animals.
[0091]3. Bleeding Procedure--Blood was obtained by puncture of the orbital sinus while under 70% CO2/30% O2 anesthesia.
[0092]4. Collection of Blood Samples-Approximately 0.5 mL of blood is collected into EDTA tubes for evaluation of hematology parameters. Approximately 1 mL of blood is collected into serum separator tubes for clinical chemistry analysis. Approximately 200 μL of plasma is obtained and frozen at ˜-80° C. for test compound/metabolite estimation. An additional ˜2 mL of blood is collected into a 15 mL conical polypropylene vial to which ˜3 mL of Trizol is immediately added. The contents are immediately mixed with a vortex and by repeated inversion. The tubes are frozen in liquid nitrogen and stored at 80° C.
Termination Procedures
Terminal Sacrifice
[0093]At the time points indicated above, rats are weighed, physically examined, sacrificed by decapitation, and exsanguinated. The animals are necropsied within approximately five minutes of sacrifice. Separate sterile, disposable instruments are used for each animal. Necropsies are conducted on each animal following procedures approved by board-certified pathologists.
[0094]Animals not surviving until terminal sacrifice are discarded without necropsy (following euthanasia by carbon dioxide asphyxiation, if moribund). The approximate time of death for moribund or found dead animals is recorded.
Postmortem Procedures
[0095]All tissues are collected and frozen within approximately 5 minutes of the animal's death. Tissues are stored at approximately -80° C. or preserved in 10% neutral buffered formalin.
Tissue Collection and Processing
[0096]Liver
1. Right medial lobe--snap freeze in liquid nitrogen and store at ˜-80° C.2. Left medial lobe--Preserve in 10% neutral-buffered formalin (NBF) and evaluate for gross and microscopic pathology.3. Left lateral lobe--snap freeze in liquid nitrogen and store at ˜-80° C.
[0097]Heart
1. A sagittal cross-section containing portions of the two atria and of the two ventricles is preserved in 10% NBF. The remaining heart is frozen in liquid nitrogen and stored at ˜-80° C.
[0098]Kidneys (Both)
1. Left--Hemi-dissect; half is preserved in 10% NBF and the remaining half is frozen in liquid nitrogen and stored at ˜-80° C.2. Right--Hemi-dissect; half is preserved in 10% NBF and the remaining half is frozen in liquid nitrogen and stored at ˜-80° C.
[0099]Testes (both)--A sagittal cross-section of each testis is preserved in 10% NBF. The remaining testes are frozen together in liquid nitrogen and stored at ˜-80° C.
[0100]Brain (whole)--A cross-section of the cerebral hemispheres and of the diencephalon are preserved in 10% NBF, and the rest of the brain is frozen in liquid nitrogen and stored at ˜-80° C.
[0101]Microarray sample preparation is conducted with minor modifications, following the protocols set forth in the Affymetrix GeneChip® Expression Technical Analysis Manual (Affymetrix, Inc. Santa Clara, Calif.). Frozen tissue is ground to a powder using a Spex Certiprep 6800 Freezer Mill. Total RNA is extracted with Trizol (Invitrogen, Carlsbad Calif.) utilizing the manufacturer's protocol. mRNA is isolated using the Oligotex mRNA Midi kit (Qiagen) followed by ethanol precipitation. Double stranded cDNA is generated from mRNA using the SuperScript Choice system (Invitrogen, Carlsbad Calif.). First strand cDNA synthesis is primed with a T7-(dT24) oligonucleotide. The cDNA is phenol-chloroform extracted and ethanol precipitated to a final concentration of 1 μg/ml. From 2 μg of cDNA, cRNA is synthesized using Ambion's T7 MegaScript in vitro Transcription Kit.
[0102]To biotin label the cRNA, nucleotides Bio-11-CTP and Bio-16-UTP (Enzo Diagnostics) are added to the reaction. Following a 37° C. incubation for six hours, impurities are removed from the labeled cRNA following the RNeasy Mini kit protocol (Qiagen). cRNA is fragmented (fragmentation buffer consisting of 200 mM Tris-acetate, pH 8.1, 500 mM KOAc, 150 mM MgOAc) for thirty-five minutes at 94° C. Following the Affymetrix protocol, 55 μg of fragmented cRNA is hybridized on the Affymetrix rat array set for twenty-four hours at 60 rpm in a 45° C. hybridization oven. The chips are washed and stained with Streptavidin Phycoerythrin (SAPE) (Molecular Probes) in Affymetrix fluidics stations. To amplify staining, SAPE solution is added twice with an anti-streptavidin biotinylated antibody (Vector Laboratories) staining step in between. Hybridization to the probe arrays is detected by fluorometric scanning (Hewlett Packard Gene Array Scanner). Data is analyzed using Affymetrix GeneChip® and Expression Data Mining (EDMT) software, the GeneExpress® database, and S-Plus® statistical analysis software (Insightful Corp.).
Identification of Toxicity Markers and Model Building using RMA and PLS Algorithms
[0103]RMA/PLS models are built as follows. From DNA microarray data from one or more studies, a matrix of RMA fold-change expression values is generated. These values are generated, for example, according to the method of Irizarry et al. (Nucl Acids Res 31(4):e15, 2003), which uses the following equation to produce a log scale linear additive model: T(PMij)=ei+aj+εij. T represents the transformation that corrects for background and normalizes and converts the PM (perfect match) intensities to a log scale. ei represents the log 2 scale expression values found on arrays i=1-I, aj represents the log scale affinity effects for probes j=1-J, and εij represents error (to correct for the differences in variances when using probes that bind with different intensities).
[0104]In RMA fold-change matrices, the rows represent individual fragments, and the columns are individual samples. A vehicle cohort median matrix is then calculated, in which the rows represent fragments and the columns represent vehicle cohorts, one cohort for each study/time-point combination. The values in this matrix are the median RMA expression values across the samples within those cohorts. Next, a matrix of normalized RMA expression values is generated, in which the rows represent individual fragments and the columns are individual samples. The normalized RMA values are the RMA values minus the value from the vehicle cohort median matrix corresponding to the time-matched vehicle cohort. PLS modeling is then applied to the normalized RMA matrix (a subset by taking certain fragments as described below), using a -1=non-tox, +1=tox supervised score vector as the dependant variable and the rows of normalized RMA matrix as the independent variables. PLS works by computing a series of PLS components, where each component is a weighted linear combination of fragment values. We use the nonlinear iterative partial least squares method to compute the PLS components.
[0105]To select fragments, a vehicle cohort mean matrix is generated, in which the rows represent fragments and the columns represent vehicle cohorts, one cohort for each study/time-point combination. The values in this matrix are the mean RMA expression values across the samples within those cohorts. A treated cohort mean matrix is then generated, in which the rows represent fragments and the columns represent treated (non-vehicle) cohorts, one cohort for each study/time-point/compound/dose combination. The values in this matrix are the mean RMA expression values across the samples within those cohorts. Next, a treated cohort fold-change matrix is generated, in which the rows represent fragments and the columns represent treated cohorts, one cohort for each study/time-point/compound/dose combination. The values in this matrix are the values in the treated cohort mean matrix minus the values in the vehicle cohort mean matrix corresponding to appropriate time-matched vehicle cohorts. Subsequently, a treated cohort p-value matrix is generated, in which the rows represent fragments and the columns represent treated cohorts, one cohort for each study/time-point/compound/dose combination. The values in this matrix are p-values based on two-sample t-tests comparing the treated cohort mean values to the vehicle cohort mean values corresponding to appropriate time-matched vehicle cohorts. This matrix is converted to a binary coding based on the p-values being less than 0.05 (coded as 1) or greater than 0.05 (coded as 0).
[0106]The row sums of the binary treated cohort p-value matrix are computed, where that row sum represents a "gene regulation score" for each fragment, representing the total number of treated cohorts where the fragment showed differential regulation (up- or down-regulation) compared to its time-matched vehicle cohort. PLS modeling and 2/3/1/3 cross-validation are then performed based on taking the top N fragments according to the regulation score, varying N and the number of PLS components, and recording the model success rate for each combination. N is chosen to be the point at which the cross-validated error rate are minimized. In the PLS model, each of those N fragments receives a PLS weight (PLS score) corresponding to the fragment's utility, or predictive ability, in the model (see Table 2 for an exemplary list of PLS scores for a kidney general toxicity model).
Example 2
Methods of Predicting at Least One Toxic Effect of a Test Agent
[0107]To determine whether or not a sample from an animal treated with a test agent or compound exhibits at least one toxic effect or response, RNA is prepared from a cell or tissue sample exposed to the agent and hybridized to a DNA microarray, as described in Example 1 above. From the nucleic acid hybridization data, a prediction score is calculated for that sample and compared to a reference score from a toxicity reference database according to the following equation. The sample prediction score=ΣwiRFCi. "i" is the index number for each gene in a gene expression profile to be evaluated. "wi" is the PLS weight (or PLS score, see Table 2 for an exemplary list of PLS scores for a general kidney toxicity model) for each gene. "RFCi" is the RMA fold-change value for the ith gene, as determined from a normalized RMA matrix of gene expression data from the sample (described above). The PLS weight multiplied by the RMA fold-change value gives a gene regulation score for each gene, and the regulation scores for all the individual genes are added to give a prediction score for the sample.
[0108]As a quality control (QC) check, for each incoming study, an average correlation assessment is performed. After the RMA matrix is generated (genes by samples), a Pearson correlation matrix is calculated of the samples to each other. This matrix is samples by samples. For each sample row of the matrix, the mean of all correlation values in that row of the matrix, excluding the diagonal (which is always 1) is calculated. This mean is the average correlation for that sample. If the average correlation is less than a threshold (for instance 0.90), the sample is flagged as a potential outlier. This process is repeated for each row (sample) in the study. Outliers flagged by the average correlation QC check are dropped out of any downstream normalization, prediction or compound similarity steps in the process.
[0109]To establish a toxicity prediction score cut-off value for a toxicity model, the true-positive and false positive rates for each possible score cut-off value are computed, using the scores from all tox and non-tox samples in the training set. This generates an ROC curve, which we use to set the cut-off score at the point on the ROC curve corresponding to ˜5% false positive rate. For example, in a kidney toxicity model of Table 2, a cut-off prediction score is about 0.318. If the sample score is about 0.318 or above, it can be predicted that the sample shows a toxic response after exposure to the test compound. If the sample score is below 0.318, it can be predicted that the sample does not show a toxic response
[0110]The model can be trained by setting a score of -1 for each gene that cannot predict a toxic response and by setting a score of +1 for each gene that can predict a toxic response. Cross-validation of RMA/PLS models may be performed by the compound-drop method and by the 2/3:1/3 method. In the compound-drop method, sample data from animals treated with one particular test compound are removed from a model, and the ability of this model to predict toxicity is compared to that of a model containing a full data set. In the 2/3:1/3 method, gene expression information from a random third of the genes in the model is removed, and the ability of this subset model to predict toxicity is compared to that of a model containing a full data set.
[0111]Compound similarity is assessed in the following way. In the same manner as described above, a cohort fold-change vector for each study/time-point/compound/dose combination is calculated. This vector is reduced to only the fragments used in the PLS predictive models. We then calculate Pearson correlations for that cohort fold-change vector with each cohort vector (also reduced to only the fragments used in the PLS predictive models) in our reference database. Finally, these Pearson correlations are ranked from highest to lowest and the results are reported.
[0112]A report may be generated comprising information or data related to the results of the methods of predicting at least one toxic effect. The report may comprise information related to the toxic effects predicted by the comparison of at least one sample prediction score to at least one toxicity reference prediction score from the database. The report may also present information concerning the nucleic acid hybridization data, such as the integrity of the data as well as information inputted by the user of the database and methods of the invention, such as information used to annotate the nucleic acid hybridization data. See PCT US02/22701 for a non-limiting example of a toxicity report that may be generated.
Example 3
Converting RMA Data from One Platform to Another
[0113]An algorithm was developed to convert probe intensity data from a first type of microarray to RMA data of a second type of microarray. This is beneficial to the customer because it provides the customer with the freedom to select the type of microarray it wishes to use with a RMA/PLS predictive model. Frequently this is the newest microarray on the market. The algorithm is beneficial for the company which builds RMA/PLS statistical models on microarray data because money and resources do not have to be expended to rebuild statistical models built on discontinued microarrays.
[0114]The conversion algorithm developed can be used on data from the Affymetrix GeneChip® rat RAE 2.0 microarray to Affymetrix GeneChip® rat RGU34 A microarray data. This conversion also allows the use of RMA/PLS toxicogenomics models built on the Affymetrix RGU34 A microarray platform to predict customer data generated on the RAE2.0 microarray platform. The conversion algorithm was tested using the liver toxicity model described in U.S. Provisional Application Ser. No. 60/559,949 and herein incorporated by reference.
[0115]The first step to using a conversion algorithm is to map microarray fragments. The RGU34 A microarray fragments which comprise the liver toxicity model were mapped to the RAE2.0 microarray. The liver toxicity model is based on 1,100 Affymetrix GeneChip® RGU34 A microarray fragments. Of the 1,100 fragments in the model, 907 were suggested by Affymetrix as matching to fragments on the RAE2.0 microarray. See Affymetrix's "User's Guide to Product Comparison Spreadsheets" which is herein incorporated by reference. Another 105 fragments mapped to fragments sharing the same RefSeq ID and 55 mapped to fragments which mapped to the same UniGene cluster. The 1067 mapping fragments were reduced to 1053. The 1053 mapped fragments represented 16 RGU34 A and 11 RAE 2.0 probes. The 47 fragments which were not mapped to the RAE2.0 microarray were assigned an RMA fold-change value of 0 for all samples and did not contribute to the prediction.
[0116]Once the microarray fragments are mapped, training samples are selected to calculate the conversion model weights. The inventors searched Gene Logic's ToxExpress® reference database, a database which is built on the Affymetrix RGU34A platform, for samples that covered a large amount of interquartile range with respect to signal intensity. Samples that covered the largest amount of variable space were selected because this method of sample selection had previously been determined by the inventors to be reliable in the development of a human sample conversion algorithm. The samples maximized Ei(Max(Xij)-Min(Xij)), where i indexes genes and j indexes samples.
[0117]The inventors found that sample size calculations were stable at a sampling of approximately 100 microarrays. For this reason, a training set consisting of 100 compounds and vehicles from rat liver tissue was selected.
[0118]The 100 training samples were used to train the weights in the conversion algorithm. This step is important because it provides for the quantitative aspect of the conversion. The weight training was performed based on a multiple regression analysis with probe values as the independent variables and RMA expression as the sum of the dependent variables.
[0119]Test samples were evaluated using the trained conversion algorithm. The multiple regression model was built on the 11 perfect match probe intensities and generated a predicted RGU34 expression value from a weighted sum of RAE 2.0 probe values. Each test array was scaled to an average probe intensity of 10 (log scale). The conversion algorithm used is given as:
Yi.sup.RGU34=βio+Σβij LOG(Xij.sup.RAE2.0/S)
where Y is the RGU34 RMA expression value for a fragment; Xij.sup.RAE2.0 for i=1 . . . 1053, j=1 . . . 11 are perfect match probe intensity values for the marker genes on the RAE2.0 microarray; S is a chip scale factor ΣijXij.sup.RAE2.0/n. Probe intensities were first floored to the minimum intensity value of 30.
[0120]Alternative approaches to using a multiple regression model exist to convert RAE2.0 data to RGU34 RMA data. Non-linear regression on probe values as well as canonical correlation of RAE2.0 probes to RGU34 A probes could be used. RMA values on a RAE2.0 microarray could be computed and then scaled or quantile-normalized to RGU34 A RMA values. In addition, although the multiple regression analysis used in this example does not take into account mismatched probes, an analysis could be used which takes into account mismatched probes.
[0121]The liver predictive model was used to compare the predictive results of test data from the RGU34 microarray to test data derived from converted RAE2.0 array data. The consistency between the RGU34 array results and the converted RAE2.0 array results was quite high. Table 3 provides the number of test samples per compound which were predicted as toxic out of the total number of samples for that compound using RGU34 RMA data and RAE2.0 converted RMA data. Amitryptilene, estradiol, amiodarone, diflunisal, phenobarbital, dioxin, ethionine, and LPS were selected as test toxicants. Clofibrate was selected because it is a rat-specific toxicant. Metformin, rosiglitazone, chlorpheniramine, and streptomycin were selected as test negative controls. The rat-specific toxicant and all of the tested negative controls correctly predicted no toxicity.
TABLE-US-00001 TABLE 3 Treatment RGU34 RAE2.0 converted Amitryptilene 1/2 2/2 Estradiol 3/3 3/3 Amiodarone 2/3 2/3 Diflunisal 2/3 2/3 Phenobarbital 3/3 3/3 Dioxin 3/3 2/3 Ethionine 3/3 3/3 LPS 3/3 3/3 Clofibrate 0/3 0/3 Metformin 0/3 0/3 Rosiglitazone 0/3 0/3 Chlorpheniramine 0/3 0/3 Streptomycin 0/3 0/3
Example 4
Database
[0122]A web-based software predictive modeling system called the ToxShield® Suite was created which is composed of a collection of RMA/PLS toxicity predictive models. Liver RMA/PLS predictive models were built to allow a user to identify and classify various toxic and mechanistic responses to unknown or test compounds. The models represent a wide variety of endpoint pathologies and indications, including general toxicity, necrosis, steatosis, macrovesicular steatosis, microvesicular steatosis, cholestasis, hepatitis, carcinogenicity, genotoxic carcinogenicity, non-genotoxic carcinogenicity, rat specific non-genotoxic carcinogenicity, peroxisome proliferation, and inducer/liver enlargement. The outcome of toxicity models represents a detailed categorization of test or unknown compounds from which mechanistic information can be inferred. Although the current models available as part of this software system are related to liver toxicity, models relating to specific toxicities of other organs including, but not limited to, liver primary cell culture, kidney, heart, spleen, bone marrow, and brain could be used.
[0123]The conversion algorithm described in Example 3 can be implemented in a software product such as the ToxShield® Suite. The customer inputs his or her data that has been generated on a microarray such as the Affymetrix RAE2.0 GeneChip® microarray platform. The software utilizes the algorithm to convert the customer's gene expression data to RMA data which is compatible with the software's toxicogenomics model built which was built exclusively on a second microarray platform such as the Affymetrix RGU34 A GeneChip® microarray. Visualizations and predictions can then be generated from the customer's data using the predictive model.
[0124]Although the present invention has been described in detail with reference to examples above, it is understood that various modifications can be made without departing from the spirit of the invention. Accordingly, the invention is limited only by the following claims. All cited patents, patent applications and publications referred to in this application are herein incorporated by reference in their entirety.
TABLE-US-00002 TABLE 1 GenBank Acc or GLGC Identifier Seq ID RefSeq ID Known Gene Name UniGene Cluster Title 25098 2 AA108277 18396 8 AA799330 Rattus norvegicus transcribed sequence with strong similarity to protein ref: NP_057030.1 (H. sapiens) CGI-17 protein; pelota (Drosophila) homolog [Homo sapiens] 18291 12 AA799497 Rattus norvegicus transcribed sequences 23063 14 AA799534 Rattus norvegicus transcribed sequences 18361 16 AA799591 Rattus norvegicus transcribed sequence with strong similarity to protein prf: 1202265A (R. norvegicus) 1202265A tubulin T beta15 [Rattus norvegicus] 14309 19 AA799676 Rattus norvegicus transcribed sequences 21007 22 AA799861 Rattus norvegicus transcribed sequence with strong similarity to protein sp.P70434 (M. musculus) IRF7_MOUSE Interferon regulatory factor 7 (IRF-7) 23203 23 AA799971 Rattus norvegicus transcribed sequence with moderate similarity to protein ref: NP_060761.1 (H. sapiens) hypothetical protein FLJ10986 [Homo sapiens] 4412 26 AA800005 CD151 antigen CD151 antigen 21035 27 AA800025 Rattus norvegicus transcribed sequence with strong similarity to protein ref: NP_542787.1 (H. sapiens) chromosome 20 open reading frame 163 [Homo sapiens] 18462 32 AA800708 Rattus norvegicus transcribed sequences 22386 37 AA800844 Rattus norvegicus transcribed sequence with moderate similarity to protein sp: P16636 (R. norvegicus) LYOX_RAT Protein-lysine 6-oxidase precursor (Lysyl oxidase) 15022 38 AA801029 nuclear receptor subfamily 2, group F, member 6 nuclear receptor subfamily 2, group F, member 6 20753 43 AA801441 platelet-activating factor acetylhydrolase beta subunit (PAF-AH beta) platelet-activating factor acetylhydrolase beta subunit (PAF-AH beta) 2109 47 AA817887 profilin profilin 9125 67 AA819338 signal sequence receptor 4 signal sequence receptor 4 8888 81 AA849036 guanylate cyclase 1, soluble, alpha 3 guanylate cyclase 1, soluble, alpha 3 1867 91 AA850940 ribosomal protein L4 ribosomal protein L4 17411 102 AA858621 CaM-kinase II inhibitor alpha CaM-kinase II inhibitor alpha 12700 104 AA858673 pancreatic secretory trypsin inhibitor type II (PSTI-II) pancreatic secretory trypsin inhibitor type II (PSTI-II) 14124 112 AA859305 tropomyosin isoform 6 tropomyosin isoform 6 4178 114 AA859536 Rattus norvegicus transcribed sequence with strong similarity to protein sp: P07153 (R. norvegicus) RIB1_RAT Dolichyl-diphosphooligosaccharide--protein glycosyltransferase 67 kDa subunit precursor (Ribophorin I) (RPN-I) 15150 115 AA859562 11852 117 AA859593 Rattus norvegicus transcribed sequence with moderate similarity to protein pdb: 1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21-Base Pair Symmetric Operator Dna, Alpha Carbons Only 4809 118 AA859616 Rattus norvegicus transcribed sequence with weak similarity to protein ref: NP_502422.1 (C. elegans) FYVE zinc finger [Caenorhabditis elegans] 19067 119 AA859663 Rattus norvegicus transcribed sequence with weak similarity to protein ref: NP_080153.1 (M. musculus) RIKEN cDNA 2310067G05 [Mus musculus] 20582 120 AA859688 Rattus norvegicus transcribed sequence with weak similarity to protein pdb: 1DUB (R. norvegicus) F Chain F, 2-Enoyl-Coa Hydratase, Data Collected At 100 K, Ph 6.5 22374 122 AA859804 Rattus norvegicus transcribed sequence with weak similarity to protein sp: P20415 (R. norvegicus) IF4E_MOUSE EUKARYOTIC TRANSLATION INITIATION FACTOR 4E (EIF-4E) (EIF4E) (MRNA CAP-BINDING PROTEIN) (EIF-4F 25 KDA SUBUNIT) 22927 127 AA859920 nucleosome assembly protein 1-like 1 nucleosome assembly protein 1-like 1 4222 132 AA860024 Rattus norvegicus transcribed sequence with strong similarity to protein sp: Q9D8N0 (M. musculus) EF1G_MOUSE Elongation factor 1-gamma (EF-1- gamma) (eEF-1B gamma) 7090 134 AA860039 Rattus norvegicus transcribed sequence 15927 137 AA866321 Rattus norvegicus transcribed sequences 11865 138 AA866383 Rattus norvegicus transcribed sequences 19402 140 AA874848 Thymus cell surface antigen Thymus cell surface antigen 16139 146 AA874927 Rattus norvegicus transcribed sequences 6451 148 AA875033 fibulin 5 fibulin 5 16419 149 AA875102 Rattus norvegicus transcribed sequence with strong similarity to protein sp: P08578 (M. musculus) RUXE_HUMAN Small nuclear ribonucleoprotein E (snRNP-E) (Sm protein E) (Sm-E) (SmE) 18084 151 AA875186 15371 152 AA875205 Rattus norvegicus transcribed sequence with strong similarity to protein sp: P55884 (H. sapiens) IF39_HUMAN Eukaryotic translation initiation factor 3 subunit 9 (eIF-3 eta) (eIF3 p116) (eIF3 p110) 15376 153 AA875206 ubiquilin 1 ubiquilin 1 15887 154 AA875225 GTP-binding protein (G-alpha-i2) GTP-binding protein (G-alpha-i2) 15888 154 AA875225 GTP-binding protein (G-alpha-i2) GTP-binding protein (G-alpha-i2) 15401 155 AA875257 Rattus norvegicus transcribed sequences 18902 158 AA875390 thioredoxin-like (32 kD) thioredoxin-like (32 kD) 15505 159 AA875414 Rattus norvegicus transcribed sequence with weak similarity to protein ref: NP_059088.1 (M. musculus) cadherin EGF LAG seven-pass G-type receptor 2 [Mus musculus] 6153 162 AA875531 24235 169 AA891286 thioredoxin reductase 1 thioredoxin reductase 1 9952 170 AA891422 hypoxia induced gene 1 hypoxia induced gene 1 9071 172 AA891578 Rattus norvegicus transcribed sequences 474 173 AA891670 Rattus norvegicus transcribed sequence with moderate similarity to protein ref: NP_034894.1 (M. musculus) mannosidase 2, alpha B1; lysosomal alpha- mannosidase [Mus musculus] 9091 174 AA891690 Rattus norvegicus transcribed sequence with strong similarity to protein ref: NP_076006.1 (M. musculus) tumor necrosis factor (ligand) superfamily, member 13 [Mus musculus] 17420 175 AA891693 Rattus norvegicus transcribed sequences 18078 176 AA891726 solute carrier family 34, member 1 solute carrier family 34, member 1 20839 177 AA891729 ribosomal protein S27a ribosomal protein S27a 11959 178 AA891735 Rattus norvegicus transcribed sequences 17693 179 AA891737 Rattus norvegicus transcribed sequences 17289 185 AA891785 Rattus norvegicus transcribed sequence with weak similarity to protein sp: P41562 (R. norvegicus) IDHC_RAT ISOCITRATE DEHYDROGENASE [NADP] CYTOPLASMIC (OXALOSUCCINATE DECARBOXYLASE) (IDH) (NADP+- SPECIFIC ICDH) (IDP) 17290 185 AA891785 Rattus norvegicus transcribed sequence with weak similarity to protein sp: P41562 (R. norvegicus) IDHC_RAT ISOCITRATE DEHYDROGENASE [NADP] CYTOPLASMIC (OXALOSUCCINATE DECARBOXYLASE) (IDH) (NADP+- SPECIFIC ICDH) (IDP) 20522 190 AA891842 Rattus norvegicus transcribed sequence with weak similarity to protein ref: NP_057713.1 (H. sapiens) hypothetical protein LOC51323 [Homo sapiens] 20523 190 AA891842 Rattus norvegicus transcribed sequence with weak similarity to protein ref: NP_057713.1 (H. sapiens) hypothetical protein LOC51323 (Homo sapiens) 17249 191 AA891858 Rattus norvegicus transcribed sequence with moderate similarity to protein sp: O88338 (M. musculus) CADG_MOUSE Cadherin-16 precursor (Kidney-specific cadherin) (Ksp-cadherin) 16023 192 AA891872 Rattus norvegicus transcribed sequence with strong similarity to protein pir: S54876 (M. musculus) S54876 NAD(P)+ transhydrogenase (B-specific) (EC 1.6.1.1) precursor-mouse 17779 194 AA891914 Rattus norvegicus transcribed sequence with moderate similarity to protein pir: A47488 (H. sapiens) A47488 aminoacylase (EC 3.5.1.14)-human 1159 197 AA891949 Rattus norvegicus transcribed sequences 17630 201 AA892012 glutamate oxaloacetate transaminase 2 glutamate oxaloacetate transaminase 2 13420 205 AA892042 Rattus norvegicus transcribed sequence with weak similarity to protein pir: JC2534 (R. norvegicus) JC2534 RVLG protein-rat 4259 207 AA892123 ribosomal protein L36 ribosomal protein L36 14595 208 AA892128 Rattus norvegicus transcribed sequences 16529 210 AA892154 Rattus norvegicus transcribed sequence with moderate similarity to protein pdb: 1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21-Base Pair Symmetric Operator Dna, Alpha Carbons Only 4482 211 AA892173 Rattus norvegicus transcribed sequence 8317 212 AA892234 Rattus norvegicus transcribed sequence with strong similarity to protein ref: NP_079845.1 (M. musculus) microsomal glutathione S-transferase 3 [Mus musculus] 4484 213 AA892258 NADPH oxidase 4 NADPH oxidase 4 18190 215 AA892280 Rattus norvegicus transcribed sequences 17717 216 AA892287 Rattus norvegicus transcribed sequence with weak similarity to protein ref: NP_061123.2 (H. sapiens) G protein-coupled receptor, family C, group 5, member C, isoform b, precursor; orphan G-protein coupled receptor; retinoic acid inducible gene 3 protein; retinoic acid responsive gene protein [Homo sapiens] 9027 218 AA892312 potassium inwardly-rectifying channel, subfamily J, member potassium inwardly-rectifying channel, subfamily J, member 16 16 13647 221 AA892367 Rattus norvegicus transcribed sequence with strong similarity to protein sp: P21531 (R. norvegicus) RL3_RAT 60S RIBOSOMAL PROTEIN L3 (L4) 820 225 AA892395 aldolase B (Rattus norvegicus transcribed sequence with strong similarity to protein sp: P00884 (R. norvegicus) ALFB_RAT FRUCTOSE-BISPHOSPHATE ALDOLASE B (LIVER-TYPE ALDOLASE), aldolase B) 12016 226 AA892404 Na+ dependent glucose transporter 1 Na+ dependent glucose transporter 1 21695 231 AA892506 coronin, actin binding protein 1A coronin, actin binding protein 1A 4499 232 AA892511 Rattus norvegicus transcribed sequence with weak similarity to protein ref: NP_077053.1 (R. norvegicus) calcium binding protein P22 [Rattus norvegicus] 8599 233 AA892522 Rattus norvegicus transcribed sequences 15154 234 AA892532 protein disulfide isomerase-related protein protein disulfide isomerase-related protein 12276 235 AA892541 Rattus norvegicus transcribed sequences 12275 235 AA892541 Rattus norvegicus transcribed sequences 18275 239 AA892572 Rattus norvegicus transcribed sequence with strong similarity to protein ref: NP_079639.1 (M. musculus) RIKEN cDNA 1110001J03 [Mus musculus] 18274 239 AA892572 Rattus norvegicus transcribed sequence with strong similarity to protein ref: NP_079639.1 (M. musculus) RIKEN cDNA 1110001J03 [Mus musculus] 4512 240 AA892578 Rattus norvegicus transcribed sequence with strong similarity to protein ref: NP_116238.1 (H. sapiens) hypothetical protein FLJ14834 [Homo sapiens] 15876 241 AA892582 aldehyde dehydrogenase family 3, member A1 aldehyde dehydrogenase family 3, member A1 17500 243 AA892616 solute carrier family 13 (sodium-dependent dicarboxylate solute carrier family 13 (sodium-dependent dicarboxylate transporter), member 3 transporter), member 3 23783 245 AA892773 Rattus norvegicus transcribed sequence with moderate similarity to protein pdb: 1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21-Base Pair Symmetric Operator Dna, Alpha Carbons Only 13542 247 AA892798 uterine sensitization-associated gene 1 protein uterine sensitization-associated gene 1 protein 22539 248 AA892799 Rattus norvegicus transcribed sequence with weak similarity to protein ref: NP_113808.1 (R. norvegicus) 3-phosphoglycerate dehydrogenase [Rattus norvegicus] 15385 249 AA892808 isocitrate dehydrogenase 3, gamma isocitrate dehydrogenase 3, gamma 23322 252 AA892821 aldo-keto reductase family 7, member A2 (aflatoxin aldo-keto reductase family 7, member A2 (aflatoxin aldehyde reductase) aldehyde reductase) 12848 257 AA892916 Rattus norvegicus Ab2-305 mRNA, complete cds 3853 260 AA892999 Rattus norvegicus transcribed sequences
3439 261 AA893000 Rattus norvegicus transcribed sequence with strong similarity to protein pir: T00335 (H. sapiens) T00335 hypothetical protein KIAA0564-human (fragment) 12020 262 AA893035 HP33 HP33 3870 266 AA893147 Rattus norvegicus transcribed sequences 548 271 AA893235 Rattus norvegicus transcribed sequence with strong similarity to protein sp: Q61585 (M. musculus) G0S2_MOUSE Putative lymphocyte G0/G1 switch protein 2 (G0S2- like protein) 17752 272 AA893244 Rattus norvegicus transcribed sequences 18967 273 AA893260 Rattus norvegicus transcribed sequence with weak similarity to protein ref: NP_083358.1 (M. musculus) RIKEN cDNA 5830411J07 [Mus musculus] 4242 276 AA893325 ornithine aminotransferase ornithine aminotransferase 7505 282 AA893702 transcobalamin II precursor transcobalamin II precursor 9084 283 AA893717 Rattus norvegicus transcribed sequence with strong similarity to protein ref: NP_036155.1 (M. musculus) Rac GTPase-activating protein 1 [Mus musculus] 10540 286 AA894027 3895 287 AA894029 Rattus norvegicus transcribed sequences 16435 290 AA894174 Rattus norvegicus transcribed sequence with strong similarity to protein pir: A31568 (R. norvegicus) A31568 electron transfer flavoprotein alpha chain precursor-rat 16849 292 AA894298 membrane metallo endopeptidase membrane metallo endopeptidase 24329 294 AA899253 myristoylated alanine rich protein kinase C substrate myristoylated alanine rich protein kinase C substrate 23778 298 AA899854 topoisomerase (DNA) 2 alpha topoisomerase (DNA) 2 alpha 9541 300 AA900505 rhoB gene rhoB gene 20711 307 AA924267 cytochrome P450, 4A1 cytochrome P450, 4A1 17157 329 AA926129 Rattus norvegicus transcribed sequence with strong similarity to protein ref: NP_446139.1 (R. norvegicus) schlafen 4 [Rattus norvegicus] 16468 330 AA926137 Rattus norvegicus transcribed sequence with strong similarity to protein ref: NP_079926.1 (M. musculus) RIKEN cDNA 0710008D09 [Mus musculus] 15028 336 AA942685 cytosolic cysteine dioxygenase 1 cytosolic cysteine dioxygenase 1 21696 346 AA944324 ADP-ribosylation factor 6 ADP-ribosylation factor 6 20812 356 AA945611 ribosomal protein L10 ribosomal protein L10 22351 361 AA945867 v-jun sarcoma virus 17 oncogene homolog (avian) v-jun sarcoma virus 17 oncogene homolog (avian) 1509 435 AB000507 aquaporin 7 aquaporin 7 17337 436 AB000717 7914 439 AB002584 beta-alanine-pyruvate aminotransferase beta-alanine-pyruvate aminotransferase 15703 444 AB009372 lysophospholipase lysophospholipase 15662 445 AB010119 t-complex testis expressed 1 t-complex testis expressed 1 4312 448 AB010635 carboxylesterase 2 (intestine, liver) carboxylesterase 2 (intestine, liver) 13973 449 AB011679 tubulin, beta 5 tubulin, beta 5 18075 454 AB013455 solute carrier family 34, member 1 solute carrier family 34, member 1 18076 454 AB013455 solute carrier family 34, member 1 solute carrier family 34, member 1 18597 455 AB013732 UDP-glucose dehydrogeanse UDP-glucose dehydrogeanse 4234 457 AB016536 (argininosuccinate lyase, heterogeneous nuclear (argininosuccinate lyase, heterogeneous nuclear ribonucleoprotein A/B) ribonucleoprotein A/B) 23625 458 AB017260 solute carrier family 22, member 5 solute carrier family 22, member 5 15243 459 AB017912 MAD homolog 2 (Drosophila) MAD homolog 2 (Drosophila) 18070 462 AF003008 max interacting protein 1 max interacting protein 1 7488 464 AF007758 synuclein, alpha synuclein, alpha 1183 465 AF013144 MAP-kinase phosphatase (cpg21) MAP-kinase phosphatase (cpg21) 16407 471 AF022247 cubilin cubilin 25165 473 AF022952 vascular endothelial growth factor B vascular endothelial growth factor B 3454 477 AF030091 cyclin L cyclin L 23045 480 AF034218 hyaluronidase 2 hyaluronidase 2 8426 483 AF036335 NonO/p54nrb homolog NonO/p54nrb homolog 17326 484 AF036548 Rgc32 protein Rgc32 protein 17327 484 AF036548 Rgc32 protein Rgc32 protein 22603 487 AF044574 2-4-dienoyl-Coenzyme A reductase 2, peroxisomal 2-4-dienoyl-Coenzyme A reductase 2, peroxisomal 20864 488 AF045464 aflatoxin B1 aldehyde reductase aflatoxin B1 aldehyde reductase 10241 489 AF048687 UDP-Gal:betaGlcNAc beta 1,4-galactosyltransferase, UDP-Gal:betaGlcNAc beta 1,4-galactosyltransferase, polypeptide 6 polypeptide 6 117 490 AF049239 sodium channel, voltage-gated, type 8, alpha polypeptide sodium channel, voltage-gated, type 8, alpha polypeptide 16649 491 AF051895 annexin 5 annexin 5 985 492 AF053312 small inducible cytokine subfamily A20 small inducible cytokine subfamily A20 4011 496 AF056333 cytochrome P450, subfamily 2E, polypeptide 1 cytochrome P450, subfamily 2E, polypeptide 1 1104 497 AF058714 solute carrier family 13, member 2 solute carrier family 13, member 2 4589 498 AF062389 kidney-specific protein (KS) kidney-specific protein (KS) 16007 499 AF062594 nucleosome assembly protein 1-like 1 nucleosome assembly protein 1-like 1 16444 502 AF065438 peptidylprolyl isomerase C-associated protein peptidylprolyl isomerase C-associated protein 16155 503 AF068860 defensin beta 1 defensin beta 1 25198 504 AF069782 Nopp140 associated protein Nopp140 associated protein 744 506 AF076856 espin espin 5496 507 AF080468 glucose-6-phosphatase, transport protein 1 glucose-6-phosphatase, transport protein 1 5497 507 AF080468 glucose-6-phosphatase, transport protein 1 glucose-6-phosphatase, transport protein 1 25204 508 AF080507 17535 513 AF090306 retinoblastoma binding protein 7 retinoblastoma binding protein 7 16156 514 AF093536 defensin beta 1 defensin beta 1 4723 515 AF093773 malate dehydrogenase 1 malate dehydrogenase 1 2368 516 AF095741 Mg87 protein Mg87 protein 2367 516 AF095741 Mg87 protein Mg87 protein 6554 517 AF097723 plasma glutamate carboxypeptidase plasma glutamate carboxypeptidase 15848 520 AI007820 Rattus norvegicus heat shock protein 90 beta mRNA, partial sequence 15849 523 AI008074 Rattus norvegicus heat shock protein 90 beta mRNA, partial sequence 15434 531 AI008836 high mobility group box 2 high mobility group box 2 15097 535 AI009405 insulin-like growth factor binding protein 3 insulin-like growth factor binding protein 3 23362 537 AI009605 Ras homolog enriched in brain Ras homolog enriched in brain 17473 544 AI009806 dynein, cytoplasmic, light chain 1 dynein, cytoplasmic, light chain 1 15616 570 AI011998 dnaJ homolog, subfamily b, member 9 dnaJ homolog, subfamily b, member 9 20817 582 AI012589 (glutathione S-transferase, pi 2, glutathione-S-transferase, (glutathione S-transferase, pi 2, glutathione-S-transferase, pi 1) pi 1) 18713 585 AI012604 eukaryotic initiation factor 5 (eIF-5) eukaryotic initiation factor 5 (eIF-5) 21950 599 AI013861 3-hydroxyisobutyrate dehydrogenase 3-hydroxyisobutyrate dehydrogenase 815 603 AI014087 ribosomal protein S26 ribosomal protein S26 15247 606 AI014169 upregulated by 1,25-dihydroxyvitamin D-3 upregulated by 1,25-dihydroxyvitamin D-3 21682 635 AI045030 CCAAT/enhancerbinding, protein (C/EBP) delta CCAAT/enhancerbinding, protein (C/EBP) delta 20802 655 AI059508 transketolase transketolase 15190 705 AI102562 Metallothionein Metallothionein 23837 707 AI102620 Rattus norvegicus transcribed sequences 4449 712 AI102838 Isovaleryl Coenzyme A dehydrogenase Isovaleryl Coenzyme A dehydrogenase 15861 714 AI102868 Rattus norvegicus phosphoserine aminotransferase mRNA, complete cds 16918 715 AI103074 ribosomal protein S12 ribosomal protein S12 20833 731 AI104035 Rattus norvegicus transcribed sequence with strong similarity to protein ref: NP_079904.1 (M. musculus) RIKEN cDNA 2010000G05 [Mus musculus] 18077 740 AI105198 solute carrier family 34, member 1 solute carrier family 34, member 1 23660 747 AI105448 hydroxysteroid 11-beta dehydrogenase 1 hydroxysteroid 11-beta dehydrogenase 1 20919 756 AI112516 zinc finger protein 36, C3H type-like 1 zinc finger protein 36, C3H type-like 1 20920 763 AI136891 zinc finger protein 36, C3H type-like 1 zinc finger protein 36, C3H type-like 1 16510 771 AI137583 17160 792 AI169370 alpha-tubulin alpha-tubulin 8749 799 AI169802 ferritin, heavy polypeptide 1 ferritin, heavy polypeptide 1 18687 804 AI170568 dodecenoyl-coenzyme A delta isomerase dodecenoyl-coenzyme A delta isomerase 21975 827 AI172247 xanthine dehydrogenase xanthine dehydrogenase 21842 828 AI172293 sterol-C4-methyl oxidase-like sterol-C4-methyl oxidase-like 15191 840 AI176456 Rattus norvegicus transcribed sequence with strong similarity to protein sp: P04355 (R. norvegicus) MT2_RAT METALLOTHIONEIN-II (MT-II) 20717 844 AI176504 glutaminase glutaminase 16518 845 AI176546 heat shock protein 86 heat shock protein 86 3431 846 AI176595 Cathepsin L Cathepsin L 17570 863 AI177683 Rattus norvegicus mRNA for hnRNP protein, partial 15259 870 AI178135 complement component 1, q subcomponent binding protein complement component 1, q subcomponent binding protein 17563 875 AI178750 eukaryotic translation elongation factor 2 eukaryotic translation elongation factor 2 17829 884 AI179576 hemoglobin beta chain complex hemoglobin beta chain complex 16081 888 AI179610 Heme oxygenase Heme oxygenase 1474 903 AI228548 Rattus norvegicus transcribed sequence with strong similarity to protein sp: P35467 (R. norvegicus) S10A_RAT S-100 protein, alpha chain 15296 907 AI228738 (FK506 binding protein 2, FK506-binding protein 1a) (FK506 binding protein 2, FK506-binding protein 1a) 17448 912 AI229637 MYB binding protein 1a MYB binding protein 1a 15862 921 AI230228 Rattus norvegicus phosphoserine aminotransferase mRNA, complete cds 17196 942 AI231519 sialyltransferase 7c sialyltransferase 7c 8212 945 AI231807 ferritin light chain 1 ferritin light chain 1 20702 946 AI231821 stathmin 1 stathmin 1 573 949 AI232087 hydroxyacid oxidase (glycolate oxidase) 3 hydroxyacid oxidase (glycolate oxidase) 3 409 953 AI232268 low density lipoprotein receptor-related protein associated low density lipoprotein receptor-related protein associated protein 1 protein 1 4574 968 AI233216 glutamate dehydrogenase 1 glutamate dehydrogenase 1 17764 985 AI234604 heat shock protein 8 heat shock protein 8 15468 997 AI235364 ribosomal protein S15a ribosomal protein S15a 15850 1018 AI236795 Rattus norvegicus heat shock protein 90 beta mRNA, partial sequence 11692 1027 AI638982 sulfotransferase family, cytosolic, 1C, member 2 sulfotransferase family, cytosolic, 1C, member 2 19997 1031 AI639043 Rattus norvegicus transcribed sequences 10071 1032 AI639058 Rattus norvegicus transcribed sequence with strong similarity to protein ref: NP_075371.1 (M. musculus) Nedd4 WW binding# protein 4; Nedd4 WW- binding protein 4 [Mus musculus] 16676 1033 AI639082 mini chromosome maintenance deficient 6 (S. cerevisiae) mini chromosome maintenance deficient 6 (S. cerevisiae) 19952 1034 AI639108 Rattus norvegicus transcribed sequences 15379 1037 AI639162 Rattus norvegicus transcribed sequences 25907 1038 AI639167 Rattus norvegicus transcribed sequences 19002 1043 AI639465 ring finger protein 28 ring finger protein 28 19943 1045 AI639479 Rattus norvegicus transcribed sequence with strong similarity to protein prf: 2008147A (R. norvegicus) 2008147A protein RAKb [Rattus norvegicus] 20082 1046 AI639488 Rattus norvegicus transcribed sequence with strong similarity to protein pir: A42772 (R. norvegicus) A42772 mdm2 protein-rat (fragments) 1203 1049 AJ000485 cytoplasmic linker 2 cytoplasmic linker 2 12422 1053 AJ006971 Death-associated like kinase Death-associated like kinase 12423 1053 AJ006971 Death-associated like kinase Death-associated like kinase 25247 1054 AJ011608 DNA primase, p49 subunit DNA primase, p49 subunit 20404 1055 AJ011656 claudin 3 claudin 3 18956 1059 D00512 acetyl-coenzyme A acetyltransferase 1 acetyl-coenzyme A acetyltransferase 1 15409 1060 D00569 2,4-dienoyl CoA reductase 1, mitochondrial 2,4-dienoyl CoA reductase 1, mitochondrial 15408 1060 D00569 2,4-dienoyl CoA reductase 1, mitochondrial 2,4-dienoyl CoA reductase 1, mitochondrial 4615 1061 D00680 glutathione peroxidase 3 glutathione peroxidase 3 18686 1062 D00729 dodecenoyl-coenzyme A delta isomerase (Rattus norvegicus mRNA for delta3, delta2-enoyl-CoA isomerase, complete cds, dodecenoyl-coenzyme A delta isomerase) 2554 1063 D00913 intercellular adhesion molecule 1 intercellular adhesion molecule 1 1306 1065 D10262 choline kinase choline kinase 3254 1070 D10756 proteasome (prosome, macropain) subunit, alpha type 5 proteasome (prosome, macropain) subunit, alpha type 5 4003 1071 D10757 proteosome (prosome, macropain) subunit, beta type 9 proteosome (prosome, macropain) subunit, beta type 9 (large
multifunctional (large multifunctional protease 2) protease 2) 23109 1072 D10854 aldo-keto reductase family 1, member A1 aldo-keto reductase family 1, member A1 24428 1074 D13126 neural visinin-like Ca2+-binding protein type 3 neural visinin-like Ca2+-binding protein type 3 15281 1075 D13623 25257 1075 D13623 1214 1076 D13871 (nuclear receptor subfamily 1, group H, member 4, solute (nuclear receptor subfamily 1, group H, member 4, solute carrier family 2, member carrier family 2, member 5) 5) 18958 1077 D13921 acetyl-coenzyme A acetyltransferase 1 acetyl-coenzyme A acetyltransferase 1 18727 1078 D13978 argininosuccinate lyase argininosuccinate lyase 11434 1079 D14014 cyclin D1 cyclin D1 18246 1081 D14441 brain acidic membrane protein brain acidic membrane protein 16768 1083 D16478 hydroxyacyl-Coenzyme A dehydrogenase/3-ketoacyl- hydroxyacyl-Coenzyme A dehydrogenase/3-ketoacyl-Coenzyme A hiolase/enoyl- Coenzyme A hiolase/enoyl-Coenzyme A hydratase Coenzyme A hydratase (trifunctional protein), alpha subunit (trifunctional protein), alpha subunit 18452 1085 D17370 CTL target antigen CTL target antigen 18453 1085 D17370 CTL target antigen CTL target antigen 16683 1086 D17445 Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, eta activation protein, eta polypeptide polypeptide 24885 1088 D25224 laminin receptor 1 (67 kD, ribosomal protein SA) laminin receptor 1 (67 kD, ribosomal protein SA) 20493 1090 D28339 3-hydroxyanthranilate 3,4-dioxygenase 3-hydroxyanthranilate 3,4-dioxygenase 16610 1091 D28557 cold shock domain protein A cold shock domain protein A 16681 1095 D37920 squalene epoxidase squalene epoxidase 5492 1097 D38061 UDP glycosyltransferase 1 family, polypeptide A6 UDP glycosyltransferase 1 family, polypeptide A6 18028 1098 D38062 UDP glycosyltransferase 1 family, polypeptide A7 UDP glycosyltransferase 1 family, polypeptide A7 1354 1099 D38065 UDP glycosyltransferase 1 family, polypeptide A1 UDP glycosyltransferase 1 family, polypeptide A1 755 1100 D38448 diacylglycerol kinase, gamma diacylglycerol kinase, gamma 25290 1102 D42148 growth arrest specific 6 growth arrest specific 6 20494 1103 D44494 3-hydroxyanthranilate 3,4-dioxygenase 3-hydroxyanthranilate 3,4-dioxygenase 20801 1104 D44495 apurinic/apyrimidinic endonuclease 1 apurinic/apyrimidinic endonuclease 1 18750 1105 D45250 protease (prosome, macropain) 28 subunit, beta protease (prosome, macropain) 28 subunit, beta 16354 1108 D50564 mercaptopyruvate sulfurtransferase mercaptopyruvate sulfurtransferase 770 1112 D83044 solute carrier family 22, member 2 solute carrier family 22, member 2 15126 1113 D83796 (UDP glycosyltransferase 1 family, polypeptide A1, UDP (UDP glycosyltransferase 1 family, polypeptide A1, UDP glycosyltransferase 1 glycosyltransferase 1 family, polypeptide A6, UDP family, polypeptide A6, UDP glycosyltransferase 1 family, polypeptide A7, UDP- glycosyltransferase 1 family, polypeptide A7, UDP- glucuronosyltransferase 1A8) glucuronosyltransferase 1A8) 17554 1115 D85100 solute carrier family 27 (fatty acid transporter), member 32 solute carrier family 27 (fatty acid transporter), member 32 13005 1116 D85189 fatty acid Coenzyme A ligase, long chain 4 fatty acid Coenzyme A ligase, long chain 4 16448 1117 D86297 aminolevulinic acid synthase 2 aminolevulinic acid synthase 2 15297 1118 D86641 (FK506 binding protein 2, FK506-binding protein 1a) (FK506 binding protein 2, FK506-binding protein 1a) 945 1120 D88666 phosphatidylserine-specific phospholipase A1 phosphatidylserine-specific phospholipase A1 25315 1121 D89730 3987 1122 D90258 proteasome (prosome, macropain) subunit, alpha type 3 proteasome (prosome, macropain) subunit, alpha type 3 1921 1123 E01524 P450 (cytochrome) oxidoreductase P450 (cytochrome) oxidoreductase 25024 1124 E03229 cytosolic cysteine dioxygenase 1 cytosolic cysteine dioxygenase 1 19824 1125 E13557 cysteine-sulfinate decarboxylase cysteine-sulfinate decarboxylase 4361 1127 H31839 BCL2-antagonist/killer 1 BCL2-antagonist/killer 1 21011 1128 H32189 glutathione S-transferase, mu 1 glutathione S-transferase, mu 1 4386 1129 H33093 Rattus norvegicus transcribed sequences 1301 1132 J02585 stearoyl-Coenzyme A desaturase 1 stearoyl-Coenzyme A desaturase 1 21012 1133 J02592 Glutathione-S-transferase, mu type 2 (Yb2) Glutathione-S-transferase, mu type 2 (Yb2) 15124 1134 J02612 (UDP glycosyltransferase 1 family, polypeptide, UDP (UDP glycosyltransferase 1 family, polypeptide A1, UDP glycosyltransferase 1 glycosyltransferase 1 family, polypeptide A6, UDP family, polypeptide A6, UDP glycosyltransferase 1 family, polypeptide A7, UDP- glycosyltransferase 1 family, polypeptide A7, UDP- glucuronosyltransferase 1A8) glucuronosyltransferase 1A8) 1174 1136 J02657 Cytochrome P450, subfamily IIC (mephenytoin 4- Cytochrome P450, subfamily IIC (mephenytoin 4-hydroxylase) hydroxylase) 16080 1138 J02722 Heme oxygenase Heme oxygenase 23699 1139 J02749 acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3- acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3-oxoacyl-Coenzyme A oxoacyl-Coenzyme A thiolase) thiolase) 23698 1139 J02749 acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3- acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3-oxoacyl-Coenzyme A oxoacyl-Coenzyme A thiolase) thiolase) 16148 1140 J02752 acyl-coA oxidase acyl-coA oxidase 1514 1142 J02780 Tropomycin 4 Tropomycin 4 21078 1143 J02791 acetyl-coenzyme A dehydrogenase, medium chain acetyl-coenzyme A dehydrogenase, medium chain 21013 1144 J02810 glutathione S-transferase, mu 1 glutathione S-transferase, mu 1 17284 1145 J02827 branched chain keto acid dehydrogenase subunit E1, alpha branched chain keto acid dehydrogenase subunit E1, alpha polypeptide polypeptide 17285 1145 J02827 branched chain keto acid dehydrogenase subunit E1, alpha branched chain keto acid dehydrogenase subunit E1, alpha polypeptide polypeptide 1762 1147 J03179 D site albumin promoter binding protein D site albumin promoter binding protein 1763 1147 J03179 D site albumin promoter binding protein D site albumin promoter binding protein 13479 1149 J03481 quinoid dihydropteridine reductase quinoid dihydropteridine reductase 13480 1149 J03481 quinoid dihydropteridine reductase quinoid dihydropteridine reductase 14997 1150 J03572 alkaline phosphatase, tissue-nonspecific alkaline phosphatase, tissue-nonspecific 16948 1151 J03588 Guanidinoacetate methyltransferase Guanidinoacetate methyltransferase 15017 1153 J03752 microsomal glutathione S-transferase 1 microsomal glutathione S-transferase 1 17394 1156 J03969 nucleophosmin 1 nucleophosmin 1 7784 1157 J04591 Dipeptidyl peptidase 4 Dipeptidyl peptidase 4 23524 1158 J04792 17393 1159 J04943 nucleophosmin 1 nucleophosmin 1 6780 1160 J05029 acetyl-Coenzyme A dehydrogenase, long-chain acetyl-Coenzyme A dehydrogenase, long-chain 4451 1161 J05031 Isovaleryl Coenzyme A dehydrogenase Isovaleryl Coenzyme A dehydrogenase 4450 1161 J05031 Isovaleryl Coenzyme A dehydrogenase Isovaleryl Coenzyme A dehydrogenase 15125 1162 J05132 (UDP glycosyltransferase 1 family, polypeptide A1, UDP (UDP glycosyltransferase 1 family, polypeptide A1, UDP glycosyltransferase 1 glycosyltransferase 1 family, polypeptide A6, UDP family, polypeptide A6, UDP glycosyltransferase 1 family, polypeptide A7, UDP- glycosyltransferase 1 family, polypeptide A7, UDP- glucuronosyltransferase 1A8) glucuronosyltransferase 1A8) 1247 1163 J05181 glutamate-cysteine ligase catalytic subunit glutamate-cysteine ligase catalytic subunit 1977 1164 J05470 Carnitine palmitoyltransferase 2 Carnitine palmitoyltransferase 2 24563 1167 J05592 protein phosphatase 1, regulatory (inhibitor) subunit 1A protein phosphatase 1, regulatory (inhibitor) subunit 1A 24564 1167 J05592 protein phosphatase 1, regulatory (inhibitor) subunit 1A protein phosphatase 1, regulatory (inhibitor) subunit 1A 18989 1168 K00136 glutathione-S-transferase, alpha type2 glutathione-S-transferase, alpha type2 634 1170 K01932 glutathione S-transferase, alpha 1 glutathione S-transferase, alpha 1 20149 1172 K03243 17758 1173 K03249 enoyl-Coenzyme A, hydratase/3-hydroxyacyl Coenzyme A enoyl-Coenzyme A, hydratase/3-hydroxyacyl Coenzyme A dehydrogenase dehydrogenase 10878 1174 K03250 ribosomal protein S11 ribosomal protein S11 20865 1175 L00117 Elastase 1 Elastase 1 1894 1176 L03201 cathepsin S cathepsin S 15411 1178 L07736 carnitine palmitoyltransferase 1 carnitine palmitoyltransferase 1 617 1179 L08831 Glucose-dependent insulinotropic peptide Glucose-dependent insulinotropic peptide 3549 1181 L11319 signal peptidase complex 18 kD signal peptidase complex 18 kD 22412 1184 L13619 growth response protein (CL-6) growth response protein (CL-6) 22413 1184 L13619 growth response protein (CL-6) growth response protein (CL-6) 109 1187 L14004 Polymeric immunoglobulin receptor Polymeric immunoglobulin receptor 1475 1190 L16764 heat shock 70 kD protein 1A heat shock 70 kD protein 1A 24770 1191 L19031 solute carrier family 21, member 1 solute carrier family 21, member 1 4749 1192 L19998 sulfotransferase family 1A, phenol-preferring, member 1 sulfotransferase family 1A, phenol-preferring, member 1 4748 1192 L19998 sulfotransferase family 1A, phenol-preferring, member 1 sulfotransferase family 1A, phenol-preferring, member 1 10248 1193 L23148 Inhibitor of DNA binding 1, helix-loop-helix protein (splice Inhibitor of DNA binding 1, helix-loop-helix protein (splice variation) variation) 43 1194 L23413 solute carrier family 26 (sulfate transporter), member 1 solute carrier family 26 (sulfate transporter), member 1 22411 1198 L26292 Kruppel-like factor 4 (gut) Kruppel-like factor 4 (gut) 15872 1201 L28135 solute carrier family 2, member 2 solute carrier family 2, member 2 15112 1205 L34049 low density lipoprotein receptor-related protein 2 low density lipoprotein receptor-related protein 2 1321 1206 L37333 glucose-6-phosphatase, catalytic glucose-6-phosphatase, catalytic 13682 1207 L38482 6406 1208 L38615 glutathione synthetase glutathione synthetase 1427 1209 L38644 karyopherin, beta 1 karyopherin, beta 1 11955 1212 L48209 cytochrome c oxidase, subunit VIIIa cytochrome c oxidase, subunit VIIIa 1920 1213 M10068 P450 (cytochrome) oxidoreductase P450 (cytochrome) oxidoreductase 15741 1214 M11670 Catalase Catalase 15189 1215 M11794 Metallothionein Metallothionein 17765 1216 M11942 heat shock protein 8 heat shock protein 8 17502 1217 M12156 heterogeneous nuclear ribonucleoprotein A1 heterogeneous nuclear ribonucleoprotein A1 6055 1218 M12337 Phenylalanine hydroxylase Phenylalanine hydroxylase 4254 1219 M12450 Group-specific component (vitamin D-binding protein) Group-specific component (vitamin D-binding protein) 7064 1220 M12919 aldolase A aldolase A 1466 1222 M14050 heat shock 70 kD protein 5 heat shock 70 kD protein 5 455 1225 M15474 tropomyosin 1, alpha tropomyosin 1, alpha 19255 1227 M15562 Rat MHC class II RT1.u-D-alpha chain mRNA, 3' end 19256 1227 M15562 Rat MHC class II RT1.u.D-alpha chain mRNA, 3' end 20809 1229 M17069 Calmodulin 2 (phosphorylase kinase, delta) Calmodulin 2 (phosphorylase kinase, delta) 25405 1230 M18330 protein kinase C, delta protein kinase C, delta 24567 1234 M19304 prolactin receptor prolactin receptor 17198 1235 M19647 kallikrein 1 kallikrein 1 17197 1235 M19647 4010 1237 M20131 20481 1240 M22631 Propionyl Coenzyme A carboxylase, alpha polypeptide Propionyl Coenzyme A carboxylase, alpha polypeptide 46 1242 M23697 Plasminogen activator, tissue Plasminogen activator, tissue 18619 1244 M24324 RT1 class lb gene RT1 class lb gene 1540 1246 M25073 alanyl (membrane) aminopeptidase alanyl (membrane) aminopeptidase 17541 1247 M26125 epoxide hydrolase 1 epoxide hydrolase 1 23225 1249 M27467 cytochrome oxidase subunit VIc cytochrome oxidase subunit VIc 11956 1250 M28255 cytochrome c oxidase, subunit VIIIa cytochrome c oxidase, subunit VIIIa 17105 1251 M29358 ribosomal protein S6 ribosomal protein S6 14346 1252 M31109 UDP-glucuronosyltransferase 2B3 precursor, microsomal UDP-glucuronosyltransferase 2B3 precursor, microsomal 1814 1253 M31174 thyroid hormone receptor alpha thyroid hormone receptor alpha 18502 1254 M31178 calbindin 1 calbindin 1 18501 1254 M31178 calbindin 1 calbindin 1 20868 1256 M32062 Fc receptor, IgG, low affinity III Fc receptor, IgG, low affinity III 20869 1256 M32062 Fc receptor, IgG, low affinity III Fc receptor, IgG, low affinity III
20298 1257 M32783 15580 1258 M33648 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 2 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 2 11755 1259 M33746 UDP-glucuronosyltransferase 2 family, member 5 UDP-glucuronosyltransferase 2 family, member 5 20126 1263 M34253 Interferon regulatory factor 1 Interferon regulatory factor 1 24590 1264 M35299 serine protease inhibitor, Kazal type 1 serine protease inhibitor, Kazal type 1 20699 1265 M35601 Fibrinogen, A alpha polypeptide Fibrinogen, A alpha polypeptide 20700 1265 M35601 Fibrinogen, A alpha polypeptide Fibrinogen, A alpha polypeptide 17661 1267 M37584 H2A histone family, member Z H2A histone family, member Z 9109 1269 M38135 Cathepsin H Cathepsin H 13723 1272 M55534 crystallin, alpha B crystallin, alpha B 4467 1274 M57664 creatine kinase, brain creatine kinase, brain 20713 1275 M57718 cytochrome P450, 4A1 cytochrome P450, 4A1 25057 1277 M58495 12606 1281 M59861 10-formyltetrahydrofolate dehydrogenase 10-formyltetrahydrofolate dehydrogenase 17378 1284 M62388 ubiquitin conjugating enzyme ubiquitin conjugating enzyme 14956 1286 M64301 mitogen-activated protein kinase 6 mitogen-activated protein kinase 6 14957 1286 M64301 mitogen-activated protein kinase 6 mitogen-activated protein kinase 6 19825 1288 M64755 cysteine-sulfinate decarboxylase cysteine-sulfinate decarboxylase 17301 1292 M69246 serine (or cysteine) proteinase inhibitor, clade H, member 1 serine (or cysteine) proteinase inhibitor, clade H, member 1 24648 1294 M74054 angiotensin receptor 1a angiotensin receptor 1a 20405 1295 M74067 claudin 3 claudin 3 240 1297 M75153 RAB11a, member RAS oncogene family RAB11a, member RAS oncogene family 23961 1298 M77694 fumarylacetoacetate hydrolase fumarylacetoacetate hydrolase 1622 1300 M80804 solute carrier family 3, member 1 solute carrier family 3, member 1 24843 1301 M80826 trefoil factor 3 trefoil factor 3 5733 1303 M81855 (ATP-binding cassette, sub-family B (MDR/TAP), member (ATP-binding cassette, sub-family B (MDR/TAP), member 1A, P- 1A, P-glycoprotein/multidrug resistance 1) glycoprotein/multidrug resistance 1) 17149 1304 M83107 Transgelin (Smooth muscle 22 protein) Transgelin (Smooth muscle 22 protein) 17150 1304 M83107 Transgelin (Smooth muscle 22 protein) Transgelin (Smooth muscle 22 protein) 4198 1305 M83143 Sialyltransferase 1 (beta-galactoside alpha-2,6- Sialyltransferase 1 (beta-galactoside alpha-2,6-sialytransferase) sialytransferase) 4199 1305 M83143 Sialyltransferase 1 (beta-galactoside alpha-2,6- Sialyltransferase 1 (beta-galactoside alpha-2,6-sialytransferase) sialytransferase) 24651 1306 M83678 RAB13 RAB13 21882 1308 M83740 6-pyruvoyl-tetrahydropterin synthase/dimerization cofactor 6-pyruvoyl-tetrahydropterin synthase/dimerization cofactor of hepatocyte nuclear of hepatocyte nuclear factor 1 alpha factor 1 alpha 23445 1310 M84719 Flavin-containing monooxygenase 1 Flavin-containing monooxygenase 1 24438 1311 M85183 angiotensin/vasopressin receptor angiotensin/vasopressin receptor 24496 1312 M85300 solute carrier family 9, member 3 solute carrier family 9, member 3 16895 1313 M86240 fructose-1,6-biphosphatase 1 fructose-1,6-biphosphatase 1 7872 1315 M86912 291 1316 M88347 Cystathionine beta synthase Cystathionine beta synthase 24615 1318 M89646 ribosomal protein S24 ribosomal protein S24 25460 1319 M89945 farensyl diphosphate synthase farensyl diphosphate synthase 11153 1320 M91652 glutamine synthetase 1 glutamine synthetase 1 25467 1321 M93297 ornithine aminotransferase ornithine aminotransferase 25468 1324 M94918 hemoglobin beta chain complex hemoglobin beta chain complex 25469 1325 M94919 1976 1326 M95493 guanylate cyclase activator 2A guanylate cyclase activator 2A 16449 1327 M95591 farnesyl diphosphate farnesyl transferase 1 farnesyl diphosphate farnesyl transferase 1 16450 1327 M95591 farnesyl diphosphate farnesyl transferase 1 farnesyl diphosphate farnesyl transferase 1 729 1328 M95762 solute carrier family 6 (neurotransmitter transporter, solute carrier family 6 (neurotransmitter transporter, GABA), member 13 GABA), member 13 1678 1331 M96674 glucagon receptor glucagon receptor 1508 1332 M97662 ureidopropionase, beta ureidopropionase, beta 23708 1335 NM_013113 ATPase Na+/K+ transporting beta 1 polypeptide ATPase Na+/K+ transporting beta 1 polypeptide 754 1336 NM_013126 diacylglycerol kinase, gamma diacylglycerol kinase, gamma 13938 1339 NM_017212 microtubule-associated protein tau microtubule-associated protein tau 1729 1342 NM_019147 jagged 1 jagged 1 15201 1349 NM_031093 18008 1350 NM_031588 neuregulin 1 neuregulin 1 16726 1352 NM_031855 Ketohexokinase Ketohexokinase 23709 1356 NM_138532 (ATPase Na+/K+ transporting beta 1 polypeptide, NME7) (ATPase Na+/K+ transporting beta 1 polypeptide, NME7) 20795 1360 NM_175761 heat shock protein 86 heat shock protein 86 5837 1363 S43408 Meprin 1 alpha Meprin 1 alpha 25064 1364 S45392 25480 1365 S46785 insulin-like growth factor binding protein, acid labile subunit insulin-like growth factor binding protein, acid labile subunit 25481 1366 S46798 4012 1367 S48325 cytochrome P450, subfamily 2E, polypeptide 1 cytochrome P450, subfamily 2E, polypeptide 1 10886 1368 S49003 5493 1369 S56936 UDP glycosyltransferase 1 family, polypeptide A6 UDP glycosyltransferase 1 family, polypeptide A6 15127 1370 S56937 (UDP glycosyltransferase 1 family, polypeptide A1, UDP (UDP glycosyltransferase 1 family, polypeptide A1, UDP glycosyltransferase 1 glycosyltransferase 1 family, polypeptide A6, UDP family, polypeptide A6, UDP glycosyltransferase 1 family, polypeptide A7, UDP- glycosyltransferase 1 family, polypeptide A7, UDP- glucuronosyltransferase 1A8) glucuronosyltransferase 1A8) 14003 1374 S65555 glutamate cysteine ligase, modifier subunit glutamate cysteine ligase, modifier subunit 355 1375 S66024 cAMP responsive element modulator cAMP responsive element modulator 356 1375 S66024 cAMP responsive element modulator cAMP responsive element modulator 16248 1376 S68135 solute carrier family 2, member 1 solute carrier family 2, member 1 15832 1377 S68589 1471 1378 S68809 S100 calcium binding protein A1 18647 1379 S69316 tumor rejection antigen gp96 9224 1381 S70011 25518 1381 S70011 15135 1382 S71021 ribosomal protein L6 ribosomal protein L6 25525 1383 S72505 glutathione S-transferase, alpha 1 glutathione S-transferase, alpha 1 18990 1384 S72506 16211 1386 S75960 uromodulin uromodulin 1943 1388 S77494 lysyl oxidase lysyl oxidase 21583 1389 S77900 25545 1389 S77900 25546 1390 S78154 10260 1393 S81497 lipase A, lysosomal acid lipase A, lysosomal acid 25563 1393 S81497 lipase A, lysosomal acid lipase A, lysosomal acid 14121 1394 S82383 tropomyosin isoform 6 tropomyosin isoform 6 3609 1395 S82579 histamine N-methyltransferase histamine N-methyltransferase 25069 1396 S82820 25070 1397 S83279 peroxisomal multifunctional enzyme type II peroxisomal multifunctional enzyme type II 18005 1401 U02320 neuregulin 1 neuregulin 1 20885 1403 U04842 epidermal growth factor epidermal growth factor 23606 1406 U05784 microtubule-associated proteins 1A/1B light chain 3 microtubule-associated proteins 1A/1B light chain 3 17806 1407 U06273 UDP-glucuronosyltransferase UDP-glucuronosyltransferase 17805 1408 U06274 UDP-glucuronosyltransferase UDP-glucuronosyltransferase 24874 1410 U07619 coagulation factor 3 coagulation factor 3 20925 1412 U08976 enoyl coenzyme A hydratase 1 enoyl coenzyme A hydratase 1 20803 1413 U09256 transketolase transketolase 646 1415 U10097 solute carrier family 12, member 3 solute carrier family 12, member 3 714 1416 U10279 solute carrier family 28 (sodium-coupled nucleoside solute carrier family 28 (sodium-coupled nucleoside transporter), member 1 transporter), member 1 1929 1418 U10357 pyruvate dehydrogenase kinase 2 pyruvate dehydrogenase kinase 2 1928 1418 U10357 pyruvate dehydrogenase kinase 2 pyruvate dehydrogenase kinase 2 16268 1419 U10894 (allograft inflammatory factor 1, balloon angioplasty (allograft inflammatory factor 1, balloon angioplasty responsive transcript) responsive transcript) 24900 1420 U12973 X transporter protein 2 X transporter protein 2 1424 1423 U14746 von Hippel-Lindau syndrome homolog von Hippel-Lindau syndrome homolog 16675 1425 U17565 mini chromosome maintenance deficient 6 (S. cerevisiae) mini chromosome maintenance deficient 6 (S. cerevisiae) 16871 1428 U18314 thymopoietin thymopoietin 22196 1433 U21719 Rattus norvegicus clone D920 intestinal epithelium proliferating cell-associated mRNA sequence 133 1436 U24174 cyclin-dependent kinase inhibitor 1A cyclin-dependent kinase inhibitor 1A 1537 1441 U27518 UDP-glucuronosyltransferase UDP-glucuronosyltransferase 1558 1442 U28504 solute carrier family 17 vesicular glutamate transporter), solute carrier family 17 vesicular glutamate transporter), member 1 member 1 1559 1442 U28504 solute carrier family 17 vesicular glutamate transporter), solute carrier family 17 vesicular glutamate transporter), member 1 member 1 20780 1444 U29881 low affinity Na-dependent glucose transporter (SGLT2) low affinity Na-dependent glucose transporter (SGLT2) 1598 1445 U30186 DNA-damage inducible transcript 3 DNA-damage inducible transcript 3 1970 1446 U31463 myosin, heavy polypeptide 9 myosin, heavy polypeptide 9 1479 1447 U32314 Pyruvate carboxylase Pyruvate carboxylase 23826 1451 U38180 solute carrier family 19, member 1 solute carrier family 19, member 1 797 1452 U38253 eukaryotic translation initiation factor 2B, subunit 3 eukaryotic translation initiation factor 2B, subunit 3 (gamma, 58 kD) (gamma, 58 kD) 19543 1455 U44948 cysteine rich protein 2 cysteine rich protein 2 16147 1459 U51898 phospholipase A2, group VI phospholipase A2, group VI 12014 1462 U54632 Ubiquitin conjugating enzyme E2I Ubiquitin conjugating enzyme E2I 989 1464 U56242 v-maf musculoaponeurotic fibrosarcoma (avian) oncogene v-maf musculoaponeurotic fibrosarcoma (avian) oncogene homolog (c-maf) homolog (c-maf) 16708 1465 U57042 adenosine kinase adenosine kinase 912 1468 U59184 bcl2-associated X protein bcl2-associated X protein 15174 1469 U59809 insulin-like growth factor 2 receptor insulin-like growth factor 2 receptor 20772 1470 U60882 heterogeneous nuclear ribonucleoproteins heterogeneous nuclear ribonucleoproteins methyltransferase-like 2 (S. cerevisiae) methyltransferase-like 2 (S. cerevisiae) 24643 1477 U68417 branched chain aminotransferase 2, mitochondrial branched chain aminotransferase 2, mitochondrial 16398 1478 U75392 B-cell receptor-associated protein 37 B-cell receptor-associated protein 37 25632 1481 U75405 collagen, type 1, alpha 1 collagen, type 1, alpha 1 1602 1483 U76379 solute carrier family 22, member 1 solute carrier family 22, member 1 20887 1484 U76635 Deoxyribonuclease I Deoxyribonuclease I 4957 1485 U76714 solute carrier family 39 (iron-regulated transporter), solute carrier family 39 (iron-regulated transporter), member 1 member 1 25643 1486 U77829 growth arrest specific 5 growth arrest specific 5 23300 1488 U84727 2-oxoglutarate carrier 2-oxoglutarate carrier 1546 1489 U85512 GTP cyclohydrolase I feedback regulatory protein GTP cyclohydrolase I feedback regulatory protein 1419 1492 U90887 arginase 2 arginase 2 22675 1493 U92081 glycoprotein 38 glycoprotein 38 17158 1496 V01227 alpha-tubulin alpha-tubulin 818 1497 X02291 aldolase B aldolase B 20818 1498 X02904 (glutathione S-transferase, pi 2, glutathione-S-transferase, (glutathione S-transferase, pi 2, glutathione-S-transferase, pi 1) pi 1) 33 1500 X03518 gamma-glutamyl transpeptidase gamma-glutamyl transpeptidase 20513 1503 X05684 pyruvate kinase, liver and RBC pyruvate kinase, liver and RBC 1551 1504 X06150 Glycine methyltransferase Glycine methyltransferase 1550 1504 X06150 Glycine methyltransferase Glycine methyltransferase 16204 1505 X06423 ribosomal protein S8 ribosomal protein S8 16205 1505 X06423 ribosomal protein S8 ribosomal protein S8 20715 1507 X07259 cytochrome P450, 4A1 cytochrome P450, 4A1 23523 1509 X07944 ornithine decarboxylase 1 ornithine decarboxylase 1
16947 1510 X08056 Guanidinoacetate methyltransferase Guanidinoacetate methyltransferase 1853 1511 X12367 Glutathione peroxidase 1 20597 1512 X12459 arginosuccinate synthetase arginosuccinate synthetase 20884 1513 X12748 epidermal growth factor epidermal growth factor 17377 1514 X13058 tumor protein p53 tumor protein p53 24778 1515 X13119 serine dehydratase serine dehydratase 16847 1516 X13549 ribosomal protein S10 ribosomal protein S10 20810 1517 X14181 25675 1517 X14181 15653 1518 X14210 ribosomal protein S4, X-linked 25676 1519 X14254 20518 1520 X14265 calmodulin 3 calmodulin 3 19244 1521 X15013 1069 1522 X15096 acidic ribosomal protein P0 acidic ribosomal protein P0 20483 1524 X15939 myosin heavy chain, polypeptide 7 myosin heavy chain, polypeptide 7 21562 1525 X15958 enoyl Coenzyme A hydratase, short chain 1 enoyl Coenzyme A hydratase, short chain 1 3202 1527 X16043 Protein phosphatase 2 (formerly 2A), catalytic subunit, Protein phosphatase 2 (formerly 2A), catalytic subunit, alpha isoform alpha isoform 25682 1530 X16933 RNA binding protein p45AUF1 RNA binding protein p45AUF1 25686 1532 X51536 ribosomal protein S3 23987 1533 X51615 20872 1534 X51707 ribosomal protein S19 9620 1535 X53377 ribosomal protein S7 ribosomal protein S7 20427 1536 X53378 ribosomal protein S13 ribosomal protein S13 25691 1537 X53504 12903 1538 X53517 CD37 antigen CD37 antigen 21122 1546 X56228 thiosulfate sulfurtransferase thiosulfate sulfurtransferase 21123 1546 X56228 thiosulfate sulfurtransferase thiosulfate sulfurtransferase 1885 1548 X56546 transcription factor 2 transcription factor 2 10860 1549 X57133 hepatocyte nuclear factor 4, alpha hepatocyte nuclear factor 4, alpha 25699 1549 X57133 hepatocyte nuclear factor 4, alpha hepatocyte nuclear factor 4, alpha 10267 1550 X57432 ribosomal protein S2 ribosomal protein S2 1037 1551 X57523 transporter 1, ATP-binding cassette, sub-family B transporter 1, ATP-binding cassette, sub-family B (MDR/TAP) (MDR/TAP) 5667 1553 X58200 ribosomal protein L23 18611 1553 X58200 ribosomal protein L23 17175 1554 X58389 10109 1555 X58465 ribosomal protein S5 25702 1555 X58465 ribosomal protein S5 25707 1558 X59677 solute carrier family 13, member 2 solute carrier family 13, member 2 21651 1560 X60767 cell division cycle 2 homolog A (S. pombe) cell division cycle 2 homolog A (S. pombe) 15875 1563 X62145 ribosomal protein L8 4441 1564 X62146 25719 1564 X62146 13646 1565 X62166 18108 1566 X62528 ribonuclease/angiogenin inhibitor ribonuclease/angiogenin inhibitor 556 1569 X64336 Protein C Protein C 20844 1570 X65228 417 1574 X70141 24640 1576 X70521 Sodium channel, nonvoltage-gated 1, alpha (epithelial) Sodium channel, nonvoltage-gated 1, alpha (epithelial) 22219 1578 X72792 alcohol dehydrogenase 1 alcohol dehydrogenase 1 24626 1581 X75856 Testis enhanced gene transcript Testis enhanced gene transcript 16272 1582 X76456 afamin afamin 24639 1584 X77932 Sodium channel, nonvoltage-gated 1, beta (epithelial) Sodium channel, nonvoltage-gated 1, beta (epithelial) 23854 1585 X78327 ribosomal protein L13 ribosomal protein L13 635 1586 X78848 glutathione S-transferase, alpha 1 glutathione S-transferase, alpha 1 13940 1587 X79321 microtubule-associated protein tau microtubule-associated protein tau 466 1588 X81395 carboxylesterase 1 carboxylesterase 1 570 1590 X82445 nuclear distribution gene C homolog (Aspergillus) nuclear distribution gene C homolog (Aspergillus) 11849 1593 X93352 ribosomal protein L10a ribosomal protein L10a 18107 1594 X94242 ribosomal protein L14 ribosomal protein L14 25770 1595 X96437 14347 1597 Y00156 UDP-glucuronosyltransferase 2B3 precursor, microsomal UDP-glucuronosyltransferase 2B3 precursor, microsomal 4594 1599 Y07704 Best5 protein Best5 protein 20173 1605 Z11932 arginine vasopressin receptor 2 arginine vasopressin receptor 2 407 1606 Z11995 low density lipoprotein receptor-related protein associated low density lipoprotein receptor-related protein associated protein 1 protein 1 439 1609 Z22607 Bone morphogenetic protein 4 Bone morphogenetic protein 4 8663 1611 Z27118 heat shock 70 kD protein 1A heat shock 70 kD protein 1A 17227 1612 Z36980 D-dopachrome tautomerase D-dopachrome tautomerase 17226 1612 Z36980 D-dopachrome tautomerase D-dopachrome tautomerase 1542 1614 Z50144 kynurenine aminotransferase 2 kynurenine aminotransferase 2 8664 1615 Z75029 R. norvegicus hsp70.2 mRNA for heat shock protein 70 15569 1616 Z78279 collagen, type 1, alpha 1 collagen, type 1, alpha 1
TABLE-US-00003 TABLE 2 GLGC Identifier PLS_Score 25024 -0.03408754 21011 0.005158207 8317 0.00286913 15861 0.01758436 15862 0.01155703 15028 -0.04786289 15154 0.01881327 15296 0.00676223 16518 0.02598835 17764 -0.02342505 20711 -0.01317801 23778 0.002304377 20795 0.00146821 20817 0.0314257 20833 -0.004259089 20919 -0.0198629 20920 -0.007400703 21012 -0.003223273 22351 -0.008960611 15848 -0.01718595 15849 -0.04416249 15850 -0.01030871 23837 -0.0118801 4312 0.003691487 20864 0.007678122 10241 0.01076413 11434 0.06352768 20801 -0.01583562 15126 -0.002417698 15297 -0.006103148 15124 0.01198701 16080 0.02010419 21013 -0.001557214 13479 -0.03089779 13480 0.003500852 6780 -0.003917337 18989 0.000967733 1475 0.01773045 1321 -0.03506051 11955 0.02492273 1920 0.01128843 15189 -0.005276864 17765 -0.02927309 4010 0.0263635 23225 0.01153367 11956 -0.009530467 11755 -0.03076732 20713 0.02154138 25057 0.01553224 17378 -0.008536189 14956 0.00635737 14957 -0.008478985 16468 0.01178596 5733 0.01442401 4748 0.00604811 4749 -0.001180088 17758 -0.01322739 1301 -0.03655559 15125 -0.005030922 17541 0.01180132 6406 0.008492458 1598 0.03642105 17805 -0.01636465 1537 -0.02368897 16768 0.005025752 17158 -0.006618596 1037 -0.03482728 17377 0.009030169 8664 0.005364025 15569 -0.01163379 15408 -0.004117654 15409 0.02009719 4615 -0.0216485 16148 -0.007715343 21078 -0.002250057 23109 0.005140497 25064 -0.02576101 1466 -0.0115101 15741 0.001858723 13723 -0.03098842 1183 0.007847724 1174 -0.02682282 1814 -0.02409571 23445 0.01268358 25069 -0.01803054 25070 -0.001117053 1247 0.002905345 17301 0.02169327 14346 0.01814763 15017 -0.005796293 634 0.02392324 17806 -0.03059827 15174 0.02558445 20887 0.003184597 20818 0.03540093 33 0.000687164 23523 0.04827108 1853 0.000184702 23987 -0.009158069 21651 -0.01072442 635 0.01430005 14347 0.007348958 25098 0.01413377 17157 0.002967211 17337 0.03499423 15703 0.003194804 15662 -0.01996508 13973 0.01031566 18075 0.001804553 18076 0.01474427 4234 -0.03231172 23625 0.008422249 15243 -0.009537201 25165 0.004905388 3454 -0.01269925 23045 -0.01042821 17326 -0.01356372 17327 -0.01550095 22603 0.01994649 117 -0.01073836 16649 -0.003848922 985 -0.004571139 4011 0.02594932 16007 -0.03245922 16155 -0.03767058 25198 -0.04053008 744 0.01448024 5496 -1.62254E-05 5497 -0.004547023 25204 0.01864999 17535 0.01886001 16156 -0.01055435 4723 -0.02257333 2367 0.00281055 2368 0.0198073 6554 -0.01628744 12422 -0.003597185 12423 -0.01363361 25247 0.02928529 20404 -0.003382577 18956 -0.03746372 2554 0.001275564 3254 -0.02432042 4003 -0.01871112 25257 -0.006161937 15281 -0.02035118 1214 0.01756383 18727 -0.01572102 18246 0.001154571 18452 -0.01337099 18453 -0.007857254 20493 0.01936436 5492 -0.01191286 18028 -0.03629819 1354 0.009908063 25290 0.02397325 20494 -0.000954101 18750 -0.02634051 25315 -0.03588133 3987 0.009837479 20149 -0.04258657 22412 -0.004335643 22413 -0.00221225 109 -0.005122522 22411 0.01450058 455 -0.01210526 25405 0.01309029 20298 -0.05332408 1622 -0.003529147 21882 0.006960723 7872 -0.01691339 24615 -0.003635782 25460 -0.007971963 25467 -0.002433017 25468 0.009742874 25469 -0.01432337 16449 -0.000927568 16450 0.004114473 5837 -0.005018729 25480 0.006534462 25481 0.03633816 4012 0.02058364 10886 -0.02500923 5493 -0.00559364 15127 0.01913647 14003 0.00302135 355 0.001723895 356 -0.01191485 16248 0.02829451 15832 -0.003373712 1471 -0.007821926 18647 -0.00834588 25518 -0.01890072 9224 -0.009229792 15135 0.03026445 25525 0.01468858 18990 0.002379164 16211 -0.01861134 1943 0.01443373 25545 -0.02041409 21583 -0.000591347 25546 -0.006230616 10260 -0.002039004 25563 -0.009749564 14121 -0.01940992 3609 0.0020902 18005 -0.000341325 16268 -0.05654464 22196 0.01060633 12014 0.006231096 16708 0.01482556 16398 0.006464105 25632 0.03466999 4957 0.008092677 25643 -0.03402377 23300 0.03958223 1546 0.01170207 22675 -0.008282468 818 -0.01053171 1550 0.01494726 1551 0.02599436 20715 0.01030098 16947 0.02858744 20884 -0.02730658 24778 -0.02842167 25675 -0.0203886 20810 -0.02795083 15653 -0.00909295 25676 -0.04245567 19244 0.01925244 1069 0.02009015 3202 0.01047109 25682 -0.03644181 25686 0.01175157 20872 0.005200382 15201 0.01743058 9620 0.009678062 20427 -0.007203343 25691 -0.01287446 25699 -0.01975985 10860 -0.01890404 10267 -0.01660402 5667 0.003279787 18611 -0.01685318 17175 0.008473313
25702 0.006244145 10109 0.005310704 25707 0.03233485 15875 0.002634939 25719 -0.01698852 4441 0.01366032 13646 0.01512804 23708 0.000573755 20844 -0.00279304 22219 0.003093927 16272 -0.004407614 25770 -0.01879616 20173 -0.007049952 407 0.004526638 8663 0.01127171 19824 1.61079E-05 1921 0.006592317 24428 0.01721819 24438 -0.00262423 18619 0.005152837 24496 -0.03948592 24567 -0.01201788 291 -0.02495906 24770 -0.008714317 24843 -0.03153809 24874 0.02920487 18686 0.01941361 43 -0.01441405 133 0.04627691 24590 -0.01762193 16675 0.03559083 13682 0.003206818 417 -0.0215943 18008 0.003835681 466 -0.003738717 24639 -0.01283457 556 -0.004202022 714 0.005186919 729 -0.003318912 770 0.01406266 797 -0.01683459 912 -0.01437363 1928 -0.007305755 1929 0.01778287 16610 0.01123602 24648 0.004198686 1104 0.02800208 1602 0.01814398 8426 -0.0182353 1203 -0.0288901 617 -0.008825291 11692 0.02179052 19997 0.002543063 10071 -0.01549941 16676 0.0117799 19952 0.004150428 15379 -0.02876546 25907 0.03277824 19002 -0.01186146 19943 0.000162394 20082 0.02651264 18078 0.000639759 20839 -0.000873427 4259 0.01316487 15385 0.01291856 4242 0.01189998 16435 -0.000204926 16849 0.02508564 15022 0.02776678 8888 0.01160653 1867 -0.00064856 24329 -0.03123893 1729 -0.03759896 9541 -0.03444796 21696 0.009596217 20812 0.0196699 13938 -0.01164793 15434 -0.006764275 15097 0.001716813 23362 -0.0179409 17473 -0.01096604 15616 0.001493839 18713 0.01234178 815 -0.02093439 15247 0.01110444 21950 0.000306391 21682 -0.006126722 20802 -0.01220903 23709 0.02399753 16510 0.03670125 4449 -0.00546298 18077 0.0171604 17160 0.01415535 2109 -0.005310179 15190 -0.01250142 16918 -0.01725919 23660 -0.01086482 8749 -0.03118036 18687 0.003382211 21975 0.01300874 21842 0.001369081 15191 0.01105956 20717 0.01063375 3431 -0.006921202 17570 0.007088764 15259 -0.01822124 17563 -0.02220618 17829 0.005354438 16081 0.0205121 1474 -0.03084054 17448 0.02467472 9125 -0.01139344 17196 -0.06969452 8212 0.02652411 20702 0.002678285 573 -0.02872789 409 -0.007299354 4574 -0.02958615 754 -0.0157468 15468 0.000192713 12700 -0.01010274 14124 -0.01342113 20126 0.0146427 4450 -0.04028917 4451 -0.04007754 17197 0.02424782 17198 0.033739 16726 0.01229342 23698 0.01072602 23699 0.005510382 1540 0.02953147 19255 -0.02175437 19256 -0.047948 20405 0.02330483 20885 -0.003796437 46 0.01204979 6055 -0.01505172 14997 -0.01111345 24563 0.002454691 24564 -0.01268496 24651 -0.0234343 240 -0.01207596 10878 -0.05290645 17105 0.02110802 1514 0.007158728 15112 -0.007915743 24900 0.000776591 9109 0.02180698 1427 -0.01731983 16683 -0.02202782 3549 -0.002275369 23524 0.02175325 19825 0.001300221 18958 -0.009980402 20803 -0.01980488 16871 -0.02941303 12606 -0.006382196 1970 -0.00636348 23826 -0.001208646 20925 0.01287874 20780 -0.009828659 16895 -0.01042923 1424 0.01814117 20481 -2.73489E-05 1542 0.01467805 17226 0.04658792 17227 0.03661337 1479 -0.02727375 1558 0.001784993 1559 -0.00440292 20753 0.000428273 20865 -0.02611805 1306 0.01473606 19543 0.01029956 15872 0.006396827 24640 0.02250593 20597 -0.0072339 439 0.002488504 20518 -0.008984546 12903 0.007889638 21562 0.002491812 10248 0.03579842 23606 -0.000202168 21122 0.005247012 21123 0.01623291 570 0.0196455 16847 0.01145459 16204 0.02414009 16205 0.008361849 23854 -0.01483347 24626 -0.0146705 1885 -0.01965638 13940 0.000886116 18108 -0.005199345 646 -0.05841963 20513 0.02871836 20483 0.002659336 11849 0.01031365 1977 0.000325571 20772 0.01157497 16448 -0.01863292 18107 0.0166564 755 -0.03462439 16681 0.0152882 4198 0.02822708 4199 0.004798302 16147 0.01038541 17554 -0.02472233 16354 0.02817476 945 0.00993543 989 -0.01391793 16407 -0.000955995 7914 0.000102491 1419 -0.04516254 24885 0.01988852 7064 -0.005395484 17149 0.02755652 17150 0.3952128 17393 -0.005221711 17394 -0.00579925 1508 -0.0102906 17284 -0.007007458 17285 0.0214901 18501 0.02471658 18502 -0.03477159 4589 -0.000894857 18597 0.005855973 4594 -0.01689378 16444 0.02065756 20809 -0.02390898 15411 0.01785927 4467 0.01709855 18070 0.01584395 7488 -0.02057392 24643 -0.001264686 1509 0.00454317 13005 -0.006822573 1894 -0.00274857 4254 -0.01411081 1762 -0.01280683 1763 -0.003490757 7784 0.002189607 23961 -0.005958063 20868 -0.01507699 20869 -0.009079757 20699 0.00043838 20700 -0.004172502 11153 -0.02787509 16948 -0.003215995 1678 0.000367942 1976 0.01736856
17502 0.01984278 17661 -0.008856236 15580 -0.02737185 17411 -0.004684325 4178 0.00538893 15150 -0.007069793 11852 -0.000403569 4809 -0.03041049 19067 -0.007720506 20582 -0.04267649 22374 -0.01256255 22927 -0.03448938 4222 -0.0165522 7090 -0.02020823 15927 6.41932E-05 11865 -0.006393904 19402 -0.04323217 16139 -0.009440685 6451 0.006511471 16419 -0.01146098 18084 -0.01723762 15371 -0.01097884 15376 -0.008551695 15887 -0.0465706 15888 -0.007077734 15401 0.03108703 18902 -0.003807752 15505 0.02092673 6153 0.005509851 4361 -0.000569115 4386 0.02562726 24235 0.000464768 9952 -0.009126578 9071 -0.000939401 474 -0.01146703 9091 -0.0287723 17420 0.002994313 11959 0.01476976 17693 0.01033417 17289 -0.003851629 17290 0.01185756 20522 0.000628409 20523 0.003173917 17249 -0.02066336 16023 0.006094849 17779 -0.000918023 1159 0.01132209 17630 0.009499276 13420 0.005331431 14595 0.02173968 16529 -0.0408304 4482 0.03541986 4484 0.02414248 18190 0.02839109 17717 0.01780007 9027 0.01143368 13647 0.001145029 820 -0.02052028 12016 0.004811067 21695 0.005617932 4499 0.00030477 8599 0.01191982 12275 0.004126427 12276 0.006840609 18274 0.000625962 18275 -0.006242172 4512 0.01254979 15876 0.0076095 17500 -0.02208598 23783 -0.003488245 13542 -0.001915889 22539 0.006842911 23322 -0.002697228 12848 -0.01525511 3853 0.02945047 3439 -0.01804814 12020 0.01677873 3870 0.007775934 548 0.01829203 17752 0.01777645 18967 -0.03837527 7505 0.00383637 9084 -0.02018928 10540 0.02506434 3895 -0.01868215 18396 0.01085198 18291 0.01498073 23063 -0.002563515 18361 0.01949046 14309 0.002836866 21007 -0.003881654 23203 0.001480229 4412 0.01905504 21035 -0.01397706 18462 -0.0280539 22386 0.01780035
Sequence CWU
0
SQTB
SEQUENCE LISTING
The patent application contains a lengthy "Sequence Listing" section. A
copy of the "Sequence Listing" is available in electronic form from the
USPTO web site
(http://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US20080281526A1).
An electronic copy of the "Sequence Listing" will also be available from
the USPTO upon request and payment of the fee set forth in 37 CFR
1.19(b)(3).
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